diff --git a/docs_nnx/guides/array_ref.ipynb b/docs_nnx/guides/array_ref.ipynb
deleted file mode 100644
index 1df71c629..000000000
--- a/docs_nnx/guides/array_ref.ipynb
+++ /dev/null
@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "15c2d208",
-   "metadata": {},
-   "source": [
-    "# Array Refs (experimental)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "99809892",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from flax import nnx\n",
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "import optax"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "787cf22a",
-   "metadata": {},
-   "source": [
-    "## Basics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d896c926",
-   "metadata": {},
-   "source": [
-    "### Array Refs 101"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cae099ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1] = ArrayRef([1, 2, 3], dtype=int32)\n",
-      "[2] = ArrayRef([2, 3, 4], dtype=int32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "a_ref = jax.new_ref(jnp.array([1, 2, 3]))\n",
-    "\n",
-    "@jax.jit\n",
-    "def increment(a_ref: jax.Ref):  # no return!\n",
-    "  array: jax.Array = a_ref[...]  # access\n",
-    "  a_ref[...] = array + 1         # update\n",
-    "\n",
-    "print(\"[1] =\", a_ref); increment(a_ref); print(\"[2] =\", a_ref)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "fb081f49",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "module @jit_increment attributes {mhlo.num_partitions = 1 : i32, mhlo.num_replicas = 1 : i32} {\n",
-      "  func.func public @main(%arg0: tensor<3xi32> {tf.aliasing_output = 0 : i32}) -> (tensor<3xi32> {jax.result_info = \"\"}) {\n",
-      "    %c = stablehlo.constant dense<1> : tensor<i32>\n",
-      "    %0 = stablehlo.broadcast_in_dim %c, dims = [] : (tensor<i32>) -> tensor<3xi32>\n",
-      "    %1 = stablehlo.add %arg0, %0 : tensor<3xi32>\n",
-      "    return %1 : tensor<3xi32>\n",
-      "  }\n",
-      "}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "@jax.jit\n",
-    "def inc(x):\n",
-    "  x[...] += 1\n",
-    "\n",
-    "print(increment.lower(a_ref).as_text())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26969861",
-   "metadata": {},
-   "source": [
-    "### Variables Refs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8c3da93c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "variable.has_ref = True\n",
-      "\n",
-      "[1] = \u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 3 (12 B)\u001b[0m\n",
-      "  \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArrayRef([1, 2, 3], dtype=int32)\n",
-      "\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "[2] = \u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 3 (12 B)\u001b[0m\n",
-      "  \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArrayRef([2, 3, 4], dtype=int32)\n",
-      "\u001b[38;2;255;213;3m)\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "variable = nnx.Variable(jnp.array([1, 2, 3]), use_ref=True)\n",
-    "print(f\"{variable.has_ref = }\\n\")\n",
-    "\n",
-    "print(\"[1] =\", variable); increment(variable); print(\"[2] =\", variable)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0a55df94",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "variable.has_ref = True\n"
-     ]
-    }
-   ],
-   "source": [
-    "with nnx.use_refs(True):\n",
-    "  variable = nnx.Variable(jnp.array([1, 2, 3]))\n",
-    "\n",
-    "print(f\"{variable.has_ref = }\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "839332be",
-   "metadata": {},
-   "source": [
-    "Mention `nnx.use_refs` can be used as global flag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b2632f1",
-   "metadata": {},
-   "source": [
-    "### Changing Status"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b7b1f421",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nnx.to_refs(model) = \u001b[38;2;79;201;177mLinear\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 6 (24 B)\u001b[0m\n",
-      "  \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 3 (12 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArrayRef\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m3\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
-      "  \u001b[38;2;156;220;254mkernel\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 3 (12 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArrayRef\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m, \u001b[38;2;182;207;169m3\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
-      "\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "nnx.to_arrays(refs_model) = \u001b[38;2;79;201;177mLinear\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 6 (24 B)\u001b[0m\n",
-      "  \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 3 (12 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m3\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
-      "  \u001b[38;2;156;220;254mkernel\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 3 (12 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m, \u001b[38;2;182;207;169m3\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
-      "\u001b[38;2;255;213;3m)\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Linear(nnx.Module):\n",
-    "  def __init__(self, in_features, out_features, rngs: nnx.Rngs):\n",
-    "    self.kernel = nnx.Param(jax.random.normal(rngs(), (in_features, out_features)))\n",
-    "    self.bias = nnx.Param(jnp.zeros(out_features))\n",
-    "\n",
-    "  def __call__(self, x):\n",
-    "    return x @ self.kernel + self.bias[None]\n",
-    "\n",
-    "model = Linear(1, 3, rngs=nnx.Rngs(0)) # without array refs\n",
-    "refs_model = nnx.to_refs(model) # convert to array refs\n",
-    "arrays_model = nnx.to_arrays(refs_model) # convert to regular arrays\n",
-    "\n",
-    "print(\"nnx.to_refs(model) =\", refs_model)\n",
-    "print(\"nnx.to_arrays(refs_model) =\", arrays_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4e35e75",
-   "metadata": {},
-   "source": [
-    "## Examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5400fe58",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Block(nnx.Module):\n",
-    "  def __init__(self, din, dmid, dout, rngs: nnx.Rngs):\n",
-    "    self.linear = Linear(din, dmid, rngs=rngs)\n",
-    "    self.bn = nnx.BatchNorm(dmid, rngs=rngs)\n",
-    "    self.dropout = nnx.Dropout(0.1, rngs=rngs)\n",
-    "    self.linear_out = Linear(dmid, dout, rngs=rngs)\n",
-    "\n",
-    "  def __call__(self, x):\n",
-    "    x = nnx.gelu(self.dropout(self.bn(self.linear(x))))\n",
-    "    return self.linear_out(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba980b6b",
-   "metadata": {},
-   "source": [
-    "### Training Loop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "566c4249",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Array(1.000178, dtype=float32)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with nnx.use_refs(True):\n",
-    "  model = Block(2, 64, 3, rngs=nnx.Rngs(0))\n",
-    "  optimizer = nnx.Optimizer(model, optax.adam(1e-3), wrt=nnx.Param)\n",
-    "\n",
-    "@jax.jit\n",
-    "def train_step(model, optimizer, x, y):\n",
-    "  graphdef, params, nondiff = nnx.split(model, nnx.Param, ...)\n",
-    "  def loss_fn(params):\n",
-    "    model =  nnx.merge(graphdef, params, nondiff)\n",
-    "    return ((model(x) - y) ** 2).mean()\n",
-    "\n",
-    "  loss, grads = jax.value_and_grad(loss_fn)(nnx.to_arrays(params))  # freeze ArrayRefs for jax.grad\n",
-    "  optimizer.update(model, grads)\n",
-    "\n",
-    "  return loss\n",
-    "\n",
-    "train_step(model, optimizer, x=jnp.ones((10, 2)), y=jnp.ones((10, 3)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1dea99c1",
-   "metadata": {},
-   "source": [
-    "### Scan Over Layers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d8136be4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "y =  [[ 0.82840395 -0.25364894]\n",
-      " [ 4.9552917   4.93638   ]\n",
-      " [-7.6721525  -3.4668717 ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "@nnx.vmap\n",
-    "def create_stack(rngs):\n",
-    "  return Block(2, 64, 2, rngs=rngs)\n",
-    "\n",
-    "with nnx.use_refs(True):\n",
-    "  block_stack = create_stack(nnx.Rngs(0).fork(split=8))\n",
-    "\n",
-    "def scan_fn(x, block):\n",
-    "  x = block(x)\n",
-    "  return x, None\n",
-    "\n",
-    "x = jax.random.uniform(jax.random.key(0), (3, 2))\n",
-    "y, _ = jax.lax.scan(scan_fn, x, block_stack)\n",
-    "\n",
-    "print(\"y = \", y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ca18a0d",
-   "metadata": {},
-   "source": [
-    "## Limitations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1dd39c79",
-   "metadata": {},
-   "source": [
-    "### MutableArray Outputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c6062d19",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error: function create_model at /var/folders/qj/tkq3kvtd66z1t36rfyj9vg0w016bdd/T/ipykernel_43144/1421484665.py:1 traced for jit returned a mutable array reference of type Ref{float32[64]} at output tree path result.bn.bias.value, but mutable array references cannot be returned.\n",
-      "\n",
-      "The returned mutable array was created on line /Users/cgarciae/repos/flax/flax/nnx/variablelib.py:250:17 (Variable.__init__).\n"
-     ]
-    }
-   ],
-   "source": [
-    "@jax.jit\n",
-    "def create_model(rngs):\n",
-    "  return Block(2, 64, 3, rngs=rngs)\n",
-    "\n",
-    "try:\n",
-    "  with nnx.use_refs(True):\n",
-    "    model = create_model(nnx.Rngs(0))\n",
-    "except Exception as e:\n",
-    "  print(f\"Error:\", e)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8bb1e9e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "model.linear = \u001b[38;2;79;201;177mLinear\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 192 (768 B)\u001b[0m\n",
-      "  \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 64 (256 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArrayRef\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m64\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
-      "  \u001b[38;2;156;220;254mkernel\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 128 (512 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArrayRef\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m2\u001b[0m, \u001b[38;2;182;207;169m64\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
-      "\u001b[38;2;255;213;3m)\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "with nnx.use_refs(False): # <-- disable array refs\n",
-    "  model = create_model(nnx.Rngs(0))\n",
-    "\n",
-    "model = nnx.to_refs(model) # convert to mutable after creation\n",
-    "\n",
-    "print(\"model.linear =\", model.linear)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3a078025",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "model.linear = \u001b[38;2;79;201;177mLinear\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 192 (768 B)\u001b[0m\n",
-      "  \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 64 (256 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArrayRef\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m64\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
-      "  \u001b[38;2;156;220;254mkernel\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 128 (512 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArrayRef\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m2\u001b[0m, \u001b[38;2;182;207;169m64\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
-      "\u001b[38;2;255;213;3m)\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "@nnx.jit\n",
-    "def create_model(rngs):\n",
-    "  return Block(2, 64, 3, rngs=rngs)\n",
-    "\n",
-    "with nnx.use_refs(True):\n",
-    "  model = create_model(nnx.Rngs(0))\n",
-    "\n",
-    "print(\"model.linear =\", model.linear)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "609bed7c",
-   "metadata": {},
-   "source": [
-    "### Reference Sharing (aliasing)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "045d03c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ValueError: only one reference to a mutable array may be passed as an argument to a function, but when tracing f at /var/folders/qj/tkq3kvtd66z1t36rfyj9vg0w016bdd/T/ipykernel_43144/1563421490.py:9 for jit the mutable array reference of type Ref{int32[]} appeared at both a and b.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_error(f, *args):\n",
-    "  try:\n",
-    "    return f(*args)\n",
-    "  except Exception as e:\n",
-    "    return f\"{type(e).__name__}: {e}\"\n",
-    "  \n",
-    "x = jax.new_ref(jnp.array(0))\n",
-    "\n",
-    "@jax.jit\n",
-    "def f(a, b):\n",
-    "  ...\n",
-    "\n",
-    "print(get_error(f, x, x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "bc2e87e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SharedVariables ValueError: only one reference to a mutable array may be passed as an argument to a function, but when tracing g at /var/folders/qj/tkq3kvtd66z1t36rfyj9vg0w016bdd/T/ipykernel_43144/1828746469.py:13 for jit the mutable array reference of type Ref{int32[]} appeared at both pytree.a.value and pytree.c.value.\n",
-      "SharedModules ValueError: only one reference to a mutable array may be passed as an argument to a function, but when tracing g at /var/folders/qj/tkq3kvtd66z1t36rfyj9vg0w016bdd/T/ipykernel_43144/1828746469.py:13 for jit the mutable array reference of type Ref{float32[1]} appeared at both pytree.d.bias.value and pytree.f.bias.value.\n"
-     ]
-    }
-   ],
-   "source": [
-    "class SharedVariables(nnx.Pytree):\n",
-    "  def __init__(self):\n",
-    "    self.a = nnx.Variable(jnp.array(0))\n",
-    "    self.b = nnx.Variable(jnp.array(1))\n",
-    "    self.c = self.a\n",
-    "\n",
-    "class SharedModules(nnx.Pytree):\n",
-    "  def __init__(self):\n",
-    "    self.d = Linear(1, 1, rngs=nnx.Rngs(0))\n",
-    "    self.e = Linear(1, 1, rngs=nnx.Rngs(0))\n",
-    "    self.f = self.d\n",
-    "\n",
-    "@jax.jit\n",
-    "def g(pytree):\n",
-    "  ...\n",
-    "\n",
-    "with nnx.use_refs(True):\n",
-    "  shared_variables = SharedVariables()\n",
-    "  shared_modules = SharedModules()\n",
-    "\n",
-    "print(\"SharedVariables\", get_error(g, shared_variables))\n",
-    "print(\"SharedModules\", get_error(g, shared_modules))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "6298f3d9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "shared variables duplicates: [[('a',), ('c',)]]\n",
-      "shared modules duplicates:   [[('d',), ('f',)]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "if (duplicates := nnx.find_duplicates(shared_variables)):\n",
-    "  print(\"shared variables duplicates:\", duplicates)\n",
-    "\n",
-    "if (duplicates := nnx.find_duplicates(shared_modules)):\n",
-    "  print(\"shared modules duplicates:  \", duplicates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "00854d38",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[38;2;79;201;177mState\u001b[0m\u001b[38;2;255;213;3m({\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
-      "  \u001b[38;2;156;220;254m'a'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArrayRef(0, dtype=int32, weak_type=True)\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
-      "  \u001b[38;2;156;220;254m'b'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArrayRef(1, dtype=int32, weak_type=True)\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
-      "\u001b[38;2;255;213;3m})\u001b[0m\n",
-      "updated \u001b[38;2;79;201;177mSharedVariables\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Variable: 2 (8 B)\u001b[0m\n",
-      "  \u001b[38;2;156;220;254ma\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArrayRef(10, dtype=int32)\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
-      "  \u001b[38;2;156;220;254mb\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArrayRef(1, dtype=int32, weak_type=True)\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
-      "  \u001b[38;2;156;220;254mc\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
-      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArrayRef(10, dtype=int32)\n",
-      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
-      "\u001b[38;2;255;213;3m)\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "@jax.jit\n",
-    "def h(graphdef, state):\n",
-    "  obj = nnx.merge(graphdef, state)\n",
-    "  obj.a[...] += 10\n",
-    "\n",
-    "graphdef, state = nnx.split(shared_variables)\n",
-    "print(state) # split deduplicates the state\n",
-    "\n",
-    "h(graphdef, state)\n",
-    "\n",
-    "print(\"updated\", shared_variables)"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md:myst"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs_nnx/guides/hijax.ipynb b/docs_nnx/guides/hijax.ipynb
new file mode 100644
index 000000000..23ae167d4
--- /dev/null
+++ b/docs_nnx/guides/hijax.ipynb
@@ -0,0 +1,572 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "15c2d208",
+   "metadata": {},
+   "source": [
+    "# Array Refs (experimental)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "99809892",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from flax import nnx\n",
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import optax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "787cf22a",
+   "metadata": {},
+   "source": [
+    "## Basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26969861",
+   "metadata": {},
+   "source": [
+    "### Variables Refs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8c3da93c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "variable.is_hijax = True\n",
+      "\n",
+      "Before = \u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 3 (12 B)\u001b[0m\n",
+      "  \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray([1, 2, 3], dtype=int32),\n",
+      "  \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "\u001b[38;2;255;213;3m)\u001b[0m\n",
+      "After = \u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 3 (12 B)\u001b[0m\n",
+      "  \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray([2, 3, 4], dtype=int32),\n",
+      "  \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "\u001b[38;2;255;213;3m)\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "variable = nnx.Variable(jnp.array([1, 2, 3]), is_hijax=True)\n",
+    "print(f\"{variable.is_hijax = }\\n\")\n",
+    "\n",
+    "@jax.jit\n",
+    "def increment(variable: nnx.Variable[jax.Array]):  # no return!\n",
+    "  new_value = variable + 1  # Array-like operations\n",
+    "  variable[...] = new_value        # in-place updates\n",
+    "\n",
+    "print(\"Before =\", variable); increment(variable); print(\"After =\", variable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "703265df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: enable once as_text is fixed\n",
+    "# print(increment.lower(variable).as_text())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0a55df94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "variable.is_hijax = True\n"
+     ]
+    }
+   ],
+   "source": [
+    "nnx.use_hijax(True)\n",
+    "\n",
+    "variable = nnx.Variable(jnp.array([1, 2, 3]))\n",
+    "\n",
+    "print(f\"{variable.is_hijax = }\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "839332be",
+   "metadata": {},
+   "source": [
+    "Mention `nnx.use_refs` can be used as global flag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b2632f1",
+   "metadata": {},
+   "source": [
+    "### Changing Status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b7b1f421",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nnx.to_hijax(model) = \u001b[38;2;79;201;177mLinear\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # HijaxVariable: 6 (24 B)\u001b[0m\n",
+      "  \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m3\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "  \u001b[38;2;156;220;254mkernel\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m, \u001b[38;2;182;207;169m3\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
+      "\u001b[38;2;255;213;3m)\u001b[0m\n",
+      "nnx.to_lojax(refs_model) = \u001b[38;2;79;201;177mLinear\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 6 (24 B)\u001b[0m\n",
+      "  \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m3\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "  \u001b[38;2;156;220;254mkernel\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m, \u001b[38;2;182;207;169m3\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
+      "\u001b[38;2;255;213;3m)\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Linear(nnx.Module):\n",
+    "  def __init__(self, in_features, out_features, rngs: nnx.Rngs):\n",
+    "    self.kernel = nnx.Param(jax.random.normal(rngs(), (in_features, out_features)))\n",
+    "    self.bias = nnx.Param(jnp.zeros(out_features))\n",
+    "\n",
+    "  def __call__(self, x):\n",
+    "    return x @ self.kernel + self.bias[None]\n",
+    "\n",
+    "with nnx.use_hijax(False): # use lojax Variables\n",
+    "  model = Linear(1, 3, rngs=nnx.Rngs(0))\n",
+    "\n",
+    "hijax_model = nnx.to_hijax(model) # convert hijax Variables\n",
+    "arrays_model = nnx.to_lojax(hijax_model) # convert to lojax Variables\n",
+    "\n",
+    "print(\"nnx.to_hijax(model) =\", hijax_model)\n",
+    "print(\"nnx.to_lojax(refs_model) =\", arrays_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4e35e75",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5400fe58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Block(nnx.Module):\n",
+    "  def __init__(self, din, dmid, dout, rngs: nnx.Rngs):\n",
+    "    self.linear = Linear(din, dmid, rngs=rngs)\n",
+    "    self.bn = nnx.BatchNorm(dmid, rngs=rngs)\n",
+    "    self.dropout = nnx.Dropout(0.1, rngs=rngs)\n",
+    "    self.linear_out = Linear(dmid, dout, rngs=rngs)\n",
+    "\n",
+    "  def __call__(self, x):\n",
+    "    x = nnx.gelu(self.dropout(self.bn(self.linear(x))))\n",
+    "    return self.linear_out(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba980b6b",
+   "metadata": {},
+   "source": [
+    "### Training Loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "566c4249",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Array(1.000178, dtype=float32)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# hijax Variables by default\n",
+    "model = Block(2, 64, 3, rngs=nnx.Rngs(0))\n",
+    "optimizer = nnx.Optimizer(model, optax.adam(1e-3), wrt=nnx.Param)\n",
+    "\n",
+    "@jax.jit\n",
+    "def train_step(model, optimizer, x, y):\n",
+    "  graphdef, params, nondiff = nnx.split(model, nnx.Param, ...)\n",
+    "  def loss_fn(params):\n",
+    "    model =  nnx.merge(graphdef, params, nondiff)\n",
+    "    return ((model(x) - y) ** 2).mean()\n",
+    "\n",
+    "  loss, grads = jax.value_and_grad(loss_fn)(nnx.to_lojax(params))  # lojax Variables for jax.grad\n",
+    "  optimizer.update(model, grads)\n",
+    "\n",
+    "  return loss\n",
+    "\n",
+    "train_step(model, optimizer, x=jnp.ones((10, 2)), y=jnp.ones((10, 3)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dea99c1",
+   "metadata": {},
+   "source": [
+    "### Scan Over Layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d8136be4",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'aval_property' object has no attribute 'spec'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;129m@jax\u001b[39m.vmap\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate_stack\u001b[39m(rngs):\n\u001b[32m      3\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m nnx.to_lojax(Block(\u001b[32m2\u001b[39m, \u001b[32m64\u001b[39m, \u001b[32m2\u001b[39m, rngs=rngs))\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m block_stack = nnx.to_hijax(\u001b[43mcreate_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnnx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mRngs\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfork\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m8\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mscan_fn\u001b[39m(x, block):\n\u001b[32m      8\u001b[39m   x = block(x)\n",
+      "    \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/flax/.venv/lib/python3.11/site-packages/jax/_src/api.py:1105\u001b[39m, in \u001b[36mvmap.<locals>.vmap_f\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m   1101\u001b[39m   api_util._check_no_aliased_ref_args(dbg, avals, args_flat)\n\u001b[32m   1103\u001b[39m axis_size_ = (axis_size \u001b[38;5;28;01mif\u001b[39;00m axis_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[32m   1104\u001b[39m               _mapped_axis_size(fun, in_tree, args_flat, in_axes_flat, \u001b[33m\"\u001b[39m\u001b[33mvmap\u001b[39m\u001b[33m\"\u001b[39m))\n\u001b[32m-> \u001b[39m\u001b[32m1105\u001b[39m explicit_mesh_axis = \u001b[43m_mapped_axis_spec\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs_flat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_axes_flat\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1106\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m spmd_axis_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m explicit_mesh_axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1107\u001b[39m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m   1108\u001b[39m       \u001b[33m\"\u001b[39m\u001b[33mOnly one of spmd_axis_name or arrays sharded on `Explicit` mesh\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1109\u001b[39m       \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m axis type is allowed. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mspmd_axis_name\u001b[38;5;132;01m=}\u001b[39;00m\u001b[33m and\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1110\u001b[39m       \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m arrays sharded on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexplicit_mesh_axis\u001b[38;5;132;01m=}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/flax/.venv/lib/python3.11/site-packages/jax/_src/api.py:1141\u001b[39m, in \u001b[36m_mapped_axis_spec\u001b[39m\u001b[34m(args_flat, in_axes_flat)\u001b[39m\n\u001b[32m   1139\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m arg, i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(args_flat, in_axes_flat):\n\u001b[32m   1140\u001b[39m   \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1141\u001b[39m     spec = \u001b[43m_get_spec\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1142\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m out_spec \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m out_spec != spec:\n\u001b[32m   1143\u001b[39m       \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m   1144\u001b[39m           \u001b[33m\"\u001b[39m\u001b[33mMapped away dimension of inputs passed to vmap should be sharded\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1145\u001b[39m           \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m the same. Got inconsistent axis specs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mout_spec\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mspec\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/flax/.venv/lib/python3.11/site-packages/jax/_src/api.py:1134\u001b[39m, in \u001b[36m_mapped_axis_spec.<locals>._get_spec\u001b[39m\u001b[34m(arg, i)\u001b[39m\n\u001b[32m   1131\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_spec\u001b[39m(arg, i):\n\u001b[32m   1132\u001b[39m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m   1133\u001b[39m     \u001b[38;5;66;03m# Duck type arrays like BCOO arrays can be passed to vmap.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1134\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mshaped_abstractify\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43msharding\u001b[49m\u001b[43m.\u001b[49m\u001b[43mspec\u001b[49m[i]\n\u001b[32m   1135\u001b[39m   \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mIndexError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[32m   1136\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[31mAttributeError\u001b[39m: 'aval_property' object has no attribute 'spec'"
+     ]
+    }
+   ],
+   "source": [
+    "@jax.vmap\n",
+    "def create_stack(rngs):\n",
+    "  return nnx.to_lojax(Block(2, 64, 2, rngs=rngs))\n",
+    "\n",
+    "block_stack = nnx.to_hijax(create_stack(nnx.Rngs(0).fork(split=8)))\n",
+    "\n",
+    "def scan_fn(x, block):\n",
+    "  x = block(x)\n",
+    "  return x, None\n",
+    "\n",
+    "x = jax.random.uniform(jax.random.key(0), (3, 2))\n",
+    "y, _ = jax.lax.scan(scan_fn, x, block_stack)\n",
+    "\n",
+    "print(\"y = \", y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ca18a0d",
+   "metadata": {},
+   "source": [
+    "## Limitations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dd39c79",
+   "metadata": {},
+   "source": [
+    "### MutableArray Outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c6062d19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: mutable hitypes should use lo_ty_qdd instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "@jax.jit\n",
+    "def create_model(rngs):\n",
+    "  return Block(2, 64, 3, rngs=rngs)\n",
+    "\n",
+    "try:\n",
+    "  model = create_model(nnx.Rngs(0))\n",
+    "except Exception as e:\n",
+    "  print(f\"Error:\", e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8bb1e9e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.linear = \u001b[38;2;79;201;177mLinear\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # HijaxVariable: 192 (768 B)\u001b[0m\n",
+      "  \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m64\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "  \u001b[38;2;156;220;254mkernel\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m2\u001b[0m, \u001b[38;2;182;207;169m64\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
+      "\u001b[38;2;255;213;3m)\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "with nnx.use_hijax(False): # <-- disable hijax Variables\n",
+    "  model = create_model(nnx.Rngs(0))\n",
+    "\n",
+    "model = nnx.to_hijax(model) # convert to mutable after creation\n",
+    "\n",
+    "print(\"model.linear =\", model.linear)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a078025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.linear = \u001b[38;2;79;201;177mLinear\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # HijaxVariable: 192 (768 B)\u001b[0m\n",
+      "  \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m64\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "  \u001b[38;2;156;220;254mkernel\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m2\u001b[0m, \u001b[38;2;182;207;169m64\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
+      "\u001b[38;2;255;213;3m)\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: why does this work?\n",
+    "@nnx.jit\n",
+    "def create_model(rngs):\n",
+    "  return Block(2, 64, 3, rngs=rngs)\n",
+    "\n",
+    "model = create_model(nnx.Rngs(0))\n",
+    "\n",
+    "print(\"model.linear =\", model.linear)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "609bed7c",
+   "metadata": {},
+   "source": [
+    "### Reference Sharing (aliasing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "045d03c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: why does this not fail?\n",
+    "def get_error(f, *args):\n",
+    "  try:\n",
+    "    return f(*args)\n",
+    "  except Exception as e:\n",
+    "    return f\"{type(e).__name__}: {e}\"\n",
+    "\n",
+    "x = nnx.Variable(jnp.array(0))\n",
+    "\n",
+    "@jax.jit\n",
+    "def f(a, b):\n",
+    "  ...\n",
+    "\n",
+    "print(get_error(f, x, x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "bc2e87e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SharedVariables None\n",
+      "SharedModules None\n"
+     ]
+    }
+   ],
+   "source": [
+    "class SharedVariables(nnx.Pytree):\n",
+    "  def __init__(self):\n",
+    "    self.a = nnx.Variable(jnp.array(0))\n",
+    "    self.b = nnx.Variable(jnp.array(1))\n",
+    "    self.c = self.a\n",
+    "\n",
+    "class SharedModules(nnx.Pytree):\n",
+    "  def __init__(self):\n",
+    "    self.d = Linear(1, 1, rngs=nnx.Rngs(0))\n",
+    "    self.e = Linear(1, 1, rngs=nnx.Rngs(0))\n",
+    "    self.f = self.d\n",
+    "\n",
+    "@jax.jit\n",
+    "def g(pytree):\n",
+    "  ...\n",
+    "\n",
+    "shared_variables = SharedVariables()\n",
+    "shared_modules = SharedModules()\n",
+    "\n",
+    "print(\"SharedVariables\", get_error(g, shared_variables))\n",
+    "print(\"SharedModules\", get_error(g, shared_modules))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6298f3d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shared variables duplicates: [[('a',), ('c',)]]\n",
+      "shared modules duplicates:   [[('d',), ('f',)]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "if (duplicates := nnx.find_duplicates(shared_variables)):\n",
+    "  print(\"shared variables duplicates:\", duplicates)\n",
+    "\n",
+    "if (duplicates := nnx.find_duplicates(shared_modules)):\n",
+    "  print(\"shared modules duplicates:  \", duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "00854d38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;2;79;201;177mState\u001b[0m\u001b[38;2;255;213;3m({\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
+      "  \u001b[38;2;156;220;254m'a'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray(0, dtype=int32, weak_type=True),\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "  \u001b[38;2;156;220;254m'b'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray(1, dtype=int32, weak_type=True),\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
+      "\u001b[38;2;255;213;3m})\u001b[0m\n",
+      "updated \u001b[38;2;79;201;177mSharedVariables\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # HijaxVariable: 2 (8 B)\u001b[0m\n",
+      "  \u001b[38;2;156;220;254ma\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray(10, dtype=int32, weak_type=True),\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "  \u001b[38;2;156;220;254mb\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray(1, dtype=int32, weak_type=True),\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m,\n",
+      "  \u001b[38;2;156;220;254mc\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mVariable\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
+      "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray(10, dtype=int32, weak_type=True),\n",
+      "    \u001b[38;2;156;220;254mis_hijax\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;86;156;214mTrue\u001b[0m\n",
+      "  \u001b[38;2;255;213;3m)\u001b[0m\n",
+      "\u001b[38;2;255;213;3m)\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "@jax.jit\n",
+    "def h(graphdef, state):\n",
+    "  obj = nnx.merge(graphdef, state)\n",
+    "  obj.a[...] += 10\n",
+    "\n",
+    "graphdef, state = nnx.split(shared_variables)\n",
+    "print(state) # split deduplicates the state\n",
+    "\n",
+    "h(graphdef, state)\n",
+    "\n",
+    "print(\"updated\", shared_variables)"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md:myst"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs_nnx/guides/array_ref.md b/docs_nnx/guides/hijax.md
similarity index 69%
rename from docs_nnx/guides/array_ref.md
rename to docs_nnx/guides/hijax.md
index 1d00c77f5..5a68162b0 100644
--- a/docs_nnx/guides/array_ref.md
+++ b/docs_nnx/guides/hijax.md
@@ -21,41 +21,31 @@ import optax
 
 +++
 
-### Array Refs 101
+### Variables Refs
 
 ```{code-cell} ipython3
-a_ref = jax.new_ref(jnp.array([1, 2, 3]))
+variable = nnx.Variable(jnp.array([1, 2, 3]), is_hijax=True)
+print(f"{variable.is_hijax = }\n")
 
 @jax.jit
-def increment(a_ref: jax.Ref):  # no return!
-  array: jax.Array = a_ref[...]  # access
-  a_ref[...] = array + 1         # update
+def increment(variable: nnx.Variable[jax.Array]):  # no return!
+  new_value = variable + 1  # Array-like operations
+  variable[...] = new_value        # in-place updates
 
-print("[1] =", a_ref); increment(a_ref); print("[2] =", a_ref)
+print("Before =", variable); increment(variable); print("After =", variable)
 ```
 
 ```{code-cell} ipython3
-@jax.jit
-def inc(x):
-  x[...] += 1
-
-print(increment.lower(a_ref).as_text())
+# TODO: enable once as_text is fixed
+# print(increment.lower(variable).as_text())
 ```
 
-### Variables Refs
-
 ```{code-cell} ipython3
-variable = nnx.Variable(jnp.array([1, 2, 3]), use_ref=True)
-print(f"{variable.has_ref = }\n")
-
-print("[1] =", variable); increment(variable); print("[2] =", variable)
-```
+nnx.use_hijax(True)
 
-```{code-cell} ipython3
-with nnx.use_refs(True):
-  variable = nnx.Variable(jnp.array([1, 2, 3]))
+variable = nnx.Variable(jnp.array([1, 2, 3]))
 
-print(f"{variable.has_ref = }")
+print(f"{variable.is_hijax = }")
 ```
 
 Mention `nnx.use_refs` can be used as global flag
@@ -73,12 +63,14 @@ class Linear(nnx.Module):
   def __call__(self, x):
     return x @ self.kernel + self.bias[None]
 
-model = Linear(1, 3, rngs=nnx.Rngs(0)) # without array refs
-refs_model = nnx.to_refs(model) # convert to array refs
-arrays_model = nnx.to_arrays(refs_model) # convert to regular arrays
+with nnx.use_hijax(False): # use lojax Variables
+  model = Linear(1, 3, rngs=nnx.Rngs(0))
+
+hijax_model = nnx.to_hijax(model) # convert hijax Variables
+arrays_model = nnx.to_lojax(hijax_model) # convert to lojax Variables
 
-print("nnx.to_refs(model) =", refs_model)
-print("nnx.to_arrays(refs_model) =", arrays_model)
+print("nnx.to_hijax(model) =", hijax_model)
+print("nnx.to_lojax(refs_model) =", arrays_model)
 ```
 
 ## Examples
@@ -99,9 +91,9 @@ class Block(nnx.Module):
 ### Training Loop
 
 ```{code-cell} ipython3
-with nnx.use_refs(True):
-  model = Block(2, 64, 3, rngs=nnx.Rngs(0))
-  optimizer = nnx.Optimizer(model, optax.adam(1e-3), wrt=nnx.Param)
+# hijax Variables by default
+model = Block(2, 64, 3, rngs=nnx.Rngs(0))
+optimizer = nnx.Optimizer(model, optax.adam(1e-3), wrt=nnx.Param)
 
 @jax.jit
 def train_step(model, optimizer, x, y):
@@ -110,7 +102,7 @@ def train_step(model, optimizer, x, y):
     model =  nnx.merge(graphdef, params, nondiff)
     return ((model(x) - y) ** 2).mean()
 
-  loss, grads = jax.value_and_grad(loss_fn)(nnx.to_arrays(params))  # freeze ArrayRefs for jax.grad
+  loss, grads = jax.value_and_grad(loss_fn)(nnx.to_lojax(params))  # lojax Variables for jax.grad
   optimizer.update(model, grads)
 
   return loss
@@ -121,12 +113,11 @@ train_step(model, optimizer, x=jnp.ones((10, 2)), y=jnp.ones((10, 3)))
 ### Scan Over Layers
 
 ```{code-cell} ipython3
-@nnx.vmap
+@jax.vmap
 def create_stack(rngs):
-  return Block(2, 64, 2, rngs=rngs)
+  return nnx.to_lojax(Block(2, 64, 2, rngs=rngs))
 
-with nnx.use_refs(True):
-  block_stack = create_stack(nnx.Rngs(0).fork(split=8))
+block_stack = nnx.to_hijax(create_stack(nnx.Rngs(0).fork(split=8)))
 
 def scan_fn(x, block):
   x = block(x)
@@ -150,28 +141,27 @@ def create_model(rngs):
   return Block(2, 64, 3, rngs=rngs)
 
 try:
-  with nnx.use_refs(True):
-    model = create_model(nnx.Rngs(0))
+  model = create_model(nnx.Rngs(0))
 except Exception as e:
   print(f"Error:", e)
 ```
 
 ```{code-cell} ipython3
-with nnx.use_refs(False): # <-- disable array refs
+with nnx.use_hijax(False): # <-- disable hijax Variables
   model = create_model(nnx.Rngs(0))
 
-model = nnx.to_refs(model) # convert to mutable after creation
+model = nnx.to_hijax(model) # convert to mutable after creation
 
 print("model.linear =", model.linear)
 ```
 
 ```{code-cell} ipython3
+# TODO: why does this work?
 @nnx.jit
 def create_model(rngs):
   return Block(2, 64, 3, rngs=rngs)
 
-with nnx.use_refs(True):
-  model = create_model(nnx.Rngs(0))
+model = create_model(nnx.Rngs(0))
 
 print("model.linear =", model.linear)
 ```
@@ -179,13 +169,14 @@ print("model.linear =", model.linear)
 ### Reference Sharing (aliasing)
 
 ```{code-cell} ipython3
+# TODO: why does this not fail?
 def get_error(f, *args):
   try:
     return f(*args)
   except Exception as e:
     return f"{type(e).__name__}: {e}"
-  
-x = jax.new_ref(jnp.array(0))
+
+x = nnx.Variable(jnp.array(0))
 
 @jax.jit
 def f(a, b):
@@ -211,9 +202,8 @@ class SharedModules(nnx.Pytree):
 def g(pytree):
   ...
 
-with nnx.use_refs(True):
-  shared_variables = SharedVariables()
-  shared_modules = SharedModules()
+shared_variables = SharedVariables()
+shared_modules = SharedModules()
 
 print("SharedVariables", get_error(g, shared_variables))
 print("SharedModules", get_error(g, shared_modules))
diff --git a/docs_nnx/index.rst b/docs_nnx/index.rst
index 58490edc1..07971470e 100644
--- a/docs_nnx/index.rst
+++ b/docs_nnx/index.rst
@@ -107,15 +107,11 @@ Basic usage
    model = Model(2, 64, 3, rngs=nnx.Rngs(0))  # eager initialization
    optimizer = nnx.Optimizer(model, optax.adam(1e-3), wrt=nnx.Param)
 
-   @nnx.jit  # automatic state management for JAX transforms
+   @nnx.jit  # automatic state propagation
    def train_step(model, optimizer, x, y):
-     def loss_fn(model):
-       y_pred = model(x)  # call methods directly
-       return ((y_pred - y) ** 2).mean()
-
+     loss_fn = lambda model: ((model(x) - y) ** 2).mean()
      loss, grads = nnx.value_and_grad(loss_fn)(model)
      optimizer.update(model, grads)  # in-place updates
-
      return loss
 
 
diff --git a/docs_nnx/nnx_basics.ipynb b/docs_nnx/nnx_basics.ipynb
index ab43de2b5..727478d45 100644
--- a/docs_nnx/nnx_basics.ipynb
+++ b/docs_nnx/nnx_basics.ipynb
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
     "    self.din, self.dout = din, dout\n",
     "\n",
     "  def __call__(self, x: jax.Array):\n",
-    "    return x @ self.w + self.b"
+    "    return x @ self.w + self.b[None]"
    ]
   },
   {
@@ -84,31 +84,10 @@
       "[[1.5643291  0.94782424 0.37971854 1.0724319  0.22112393]]\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/cgarciae/repos/flax/.venv/lib/python3.11/site-packages/treescope/renderers.py:251: UserWarning: Ignoring error inside wrapper hook <function use_autovisualizer_if_present at 0x117717600>:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/Users/cgarciae/repos/flax/.venv/lib/python3.11/site-packages/treescope/renderers.py\", line 225, in _render_subtree\n",
-      "    postprocessed_result = hook(\n",
-      "                           ^^^^^\n",
-      "  File \"/Users/cgarciae/repos/flax/.venv/lib/python3.11/site-packages/treescope/_internal/handlers/autovisualizer_hook.py\", line 47, in use_autovisualizer_if_present\n",
-      "    result = autoviz(node, path)\n",
-      "             ^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/Users/cgarciae/repos/flax/.venv/lib/python3.11/site-packages/treescope/_internal/api/array_autovisualizer.py\", line 306, in __call__\n",
-      "    jax.sharding.PositionalSharding\n",
-      "  File \"/Users/cgarciae/repos/flax/.venv/lib/python3.11/site-packages/jax/_src/deprecations.py\", line 54, in getattr\n",
-      "    raise AttributeError(message)\n",
-      "AttributeError: jax.sharding.PositionalSharding was deprecated in JAX v0.6.0 and removed in JAX v0.7.0\n",
-      "\n",
-      "  warnings.warn(\n"
-     ]
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<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_d5660a98b46e49b8a1073c1b8504eadb\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -120,7 +99,7 @@
     {
      "data": {
       "text/html": [
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Ynzi9YROqppG0CiJmztflBng55Ny0dGXcn2dE4Tyr1ivf5w8FVuAX0nEAaXN/ZXc4392v7wyvrC+zPTH0BMiTaWYqschUNyYUiN66AZFmVhg2N9Q5k291LaPSDHxDO91yjALBLfe4eVsXjnn9nhuQdAP8S0Gy4XhT+WPiu4OldkoRdkqhdCyn5w7b412ro/CEiQd4gO9xeS5vazCcr5fhISGD6oSxoQnvo2fs8NFCPqizrVJOb7hDizS0cBerQ1sYOWXftgRh9gynO8B4/14/QSmFdekADD6lNgIr5y0KEgfnsocgD8SPmgfqsc4hdyHK+UQyR1AD2djkaHsqbkv422Z4PoyVU7+Fx6ojAqNzioeW7qT1E24VMNgitRZc+Sg5CIBPDefLQl6TqtKJQOO5Yg3zAcD6A12/sU6tL74os2voGXayyxEEbW65xw73vcq6naKjig6aPzJjKrjxYp88S1ZxLuveQ40qbhEJ3QL75kBq3MLAwdTSzqFgwiBSYWu1WjHYVPUtRwSZj4tzyVDD5eLKOqz2VZVNtRpqNTXZqbx3t/IOXVPhTqeFMgMqrRqhtZvxaxaeta9TsLfe1y8db7ThVtOd80Icxv0IWmmVsuR5w9qr7tpQxoF1vdo+xImi/k+DyuIQdXBe+jS1+lqrretmbUVxrlVdXTfpqvJlvUVP101aWquja6OOrut1DIWEGmqWUrsJ2aieO6uLYr6udaVcNynlXm0JdZO1+sswhPeiMCZ/5U6JO24AzPI0eU9qFvPrKD/zlgic/bLyUrIV+p8SamrZC1zgtX/TKXavcWLjlTXtOWEL6cVRsS6aZB3D6LcNpsh9JOugO7H+8Ac0U3FvQgOGNK7WodRNqaZ51K2dR93P8+jnebRhHr34dPPo3m6Tp1szebqfJ8/f9eR58QkmT/q/HAwrb1QhuRKnaMXkg/is7leSMnBCNwU52nwpW7BQF2krKd0zSoZbTrZHxuTIXWWRVo8IFpJgWxNe4bIW3ndwW3fMgfrJ0o1ENPhAAzBiIsNbPtyx4YS9jHVbg6UKBPc54/U1iM8/YuqF3JHLGH95j9IEIPfU4fZCmaKk6LaEZbgNR45lNlth4uBctopyKeb9ySIrYnGYESnj/9BBdwqnWJw3NSRO6Y3vGb5RkDZqFAW1HfP5fVOVuxRCNX5TSh/H+QvmLHW71Yo3LS4VMJRZa/swz4N6SbCKVpkKT29/KnDoNxlPr1/59WtBb1w57pqkgXaNl4z4mPNd2aZf3kOGBA4Maz6io2A+7x8M6aJcLFKHS4Nay+Pvb68i0PNkEcJXo4rcPlxFbndSka32qq4jMkKjklRr+DAlURB/P0oi7lnT46Mdyxjm7HB2yqDlpHGBsrwSE+P50j1B5RYsDF3GwTNwOIPaZcAgvLHb6l2KYUypSuty+ooKK/j+9CllBY2t+IkFvpI0vcCzWI20pYtR7bERtFiJ3AEWrwZ+QW8Gpt68uBu4BjpPvTjDzeY/peGcBQDyZAljwKyOPkxcr9JkSdL8tmWHC29OuilB7Q/jOV7xQvfcgJACu70DgW5XXEDa/TVJFkjA3RERbxvr0ouQM7BMKOZgubaFuFlzqKJ+63uR37rx0pZWLlrNX93Jy8Sb5VqcCpQpFS2xGykGXkNLXE9LDxKgzMA8sIWyMPydG0kGV+RE70lG4ZhYtauLz7rQ+R2/P0BZRa2/uqsM/Bt6gWNvSBY4zkJ9RYVr6P0pWUrk1juRq+xiiKIfvCmJ5M0dfFWJpr/Sjj7wuQXX3xfQLygMDanRpfMIvyrr5zRFaBBefvUGOxJKfynF02QodlPyU3rbNsI5PRdqIN2ZbMRCHWZXH0lDkU1Fg9e2uXRZtuX0jnBwrTZiGzWOZahdoi2Oo2x++z1DtD7aqermUdMkCblHiCubjSKLZHWs74Tca5H6n95evKUYIddxvgZ9++ouRP2j6mfG4+OJVNttnOimaxmhNDCHUdBnbJAXTgTNMLGCsE/wBnaUmTQiWEZF51peeLSVKUvNL3oY210kZ25+871U/80qpYyaaZIDeRw2uydOQOa2kbZhXOYaleKwbywmVeaPnTXOpKld6+Gaf28NrhrWjSpsUE5VJjto555qQYOq/vBglSjRt/YuCXQHFapAT8vRvx5IaEETDIz9Ym3Ixvauh1RUt1DKbkK1EpVY0UpNleXeX1J+6BBhoCBNxgzmjgKdSvXo4EkrTKK0N+26HYA08PI0WZOs3oL/KB+hLKDnR16W/RBmeQ8sFrB041mCouTPMBSW033CQxyM3XLYuE2Ue0FsV/EbzpSm+NpKgsT6LtXGZ0ls6RZnrSxOo/X2X+Ov7so+srl8q23gwQcwKqzVFUpfzpB6JUPmUVTLZo9p2Fqu2IpTlRIHYHvE6rO9tSpuVwPIcrKU1zbMomDiZCjbpGZPLFuTE9ObB8qJIRdyou+i2FpmvZg4QJ2YRHatmDiALqYiuephob06cPxrvF0l4xNEOUM0yZcRrcrOx8Ap5DyD8QYfKqHPVZCbXDHYK7IAgB5UdU5yllSGPjTdqgfcU1eq67ct8ysmdEpK95biQjXxf73JYez5Fi9uwIGIwHApdKNjEorerx6CXB10MYoTAYVPOORyiuX4I1J6dCOdXdgfC3Ygzq6cXcervmGoxYP8eKlNb0jP1Lt4CY28QqDW4Kcln8YV/ouyd2E9oSTkgVMlXmhUcW+4fF4GzanqQLDjMFAiGwcCKZv1dNs15NBObvcNWbx72158W80UtTIcqJJJkPxJnqfhFCbzlk1buCM3rRaImyX40tanC/BxisaZWwNpduoFlBjN/yXBx7O6OhGeXQrAnG8fUH+enkyeJe1qHKN4pqm2g1WNsJ0EIggzifwIAxXWU6TaY50HbSL/dDzgYFTlAe/D0oDuF66ruh8aoZ2jdVVK3AIrHnH6hHFoQbPOxCwA1PCcdM36+iV91w0mDm2kwwAQbpyisX5sL5iHnuKTXmE8fxaFwMxr5VCwtOQTg1v0WojrqztBibsq3YI0jb2AoN4aV4REDECy+Wt2ghQmrgjTSTjqEg8H6VEfQXOgCq4N8HKs1ewiobTlg9pYC2rnchlqpLYIky3hFPhC+y5K0bEU7fKtbexqdr7KPQtUtGiQom3RLAy+t7pOb4j2l9vrk4V2ScTH1NBS9YTHbyvKUgqBawsenPKv5Xv2DHtRHkzaUQMn+q4SRc9nq0hUXtBu0uTSv92mx5xogYDbebbKU+JGPTU1y0/LlzsEFK1yRxRGvykPcuTJ0oQFySUSfFFwqFxPy9OzBRbNKPHoVwVTvCpZRWU5JS77LiFvDO+zNC07SHpAQ79dSxGJPBw1LThUwl3sQUwagSsKwKUViT58VeJbNfSloVOVoDx0pnw1aBuxYgVBpsaDOBI5llLS22in0nFiAmpovtAtQ1m5r4Vp7WoJ0wzB/BdpsnjDLVPzMUJldwDedhK8WrPrM/h6OE1tfQjjIPnQC8gNeBi0VAqkXM5fA4OvXSmbe/RiXOuxXBR+3Vac+v7PsgB4ErxbZfmCxQC1gmqp1r+442fZq/WfqNtHHylJM3aev6Xb0IocZCyt7ipBV68OYivoj7RKtK2vpSc1zisXnHzckq75Csx70fRWeWLXNBNds0Q9lGsob0J7ZGzJuk61ZcEZ+hgtULq4SrawlCU0xt8HL/evJb3VewzflEKCUL1omKvVAtFfYm7rbSslNKBMXyn96q5G8TbBEocgLSwScbe+Ur7Fyua3xmghAP+aOo2dAl2ajUzjgTxbaRWX4hbiAyu4GnioK3VTcSLrgxZqJRsGt+ICuShg68wSeXabEyQL0OY67yktl/lpEkVPFZvsjk66phLwOUPKQceakmvvJsSHXG28YsmLc3ujlyE70rSc7+M8+UtIPrTuDOhAM0r895ASEw+UqCS4UQ+9V+W5SFYZ1QyUqR5DE5cf4T5TkJy84RRDW6xyf+tY5ZeflTFNYBpOQBfuKOuW/LFeGvhNYiLdIKl5cXWAdZdBF6chr260axKLzUdFzXYplAq6nj22Fq1Y6VvcTbpplh6IrllDq5SRa9bL9iLogY/tRWBzqAti5psnlbLrBdxwZU57vIMi6KKWghNgGgHEkZ6HFhMJxEJZQZmpJks+KAiYkb8TUQINm6eb0OmVYmV3gJ4u83FhOdY33ygik4EPNGDmekkcqw6iJi2+9cFW7rvVYLh3rHmOuy0dq9USVdd0x+C8Gdk0crBT0Qd1RddVVpHIpqalfi5a6jvhnDc01c9FUxXQclt9Z/DmNd5ov21uK7Fe/JGN9fPHNFZ1eLlHW/18j7YqF8ft2iMgO01fCbMI9NlrxylmpwlmKyM0TrdtDn0tjho8cCYV+NXptHoqBgEePjlelbMiK3Ss36h6dVO9AbaZC/PNHkUAVJ157GeYQYJTDJZr2rjlPmS9MpXrTkw/u01WlvpilvlIkXqiaptMag5iVdD0dwHMN4TspKsfrgmJTLoqxkcvyqHcamArIFHu/YwuBsNt91hKyRVi8+vln5OZB4qjh0TLt6grRnsb314o8xlt7d2bRktflkkVEPe1a+xy1wBmDYT7AfiOdnTZi/vpZESF0Fg/CF3l6MIabKtd99wa6PqnlHpWwy7eZlO5zkZlV/l6oJ5gNB31q63HmdXdWpGDbRW5qKtIGN+rIt3tFTEsZsokdveBqx8etvRSdSrv9Egu7Zg+xf1ZjvthCNd6rOnwI23Ma2kSUqCVOC4LNkuF/U1f7vmUpZl82bK9DNfVLr0sC2/IKXvAZaOsiZl2UTQ1oDGAUbmwlgWqforJc/5sz6e7rla9XUi+aNSw+ZUFLziQSDHAFeFiGZQljtXrmapX6O1+iV7lGj31ErzqtXmVfMZCru8Wq78Yr3ovEn1Qgr3bwCd/vGsCtAS3RJxKDy2B5vBZVzsZWxwsesAu0tojPK7vLe06qPL0TiPYgkYcWZvbTm80JItaWMliDWO8vaOres6VdQXzURMzMBrHs4geBrZvmErXwjafNjJIrPYchQ6JtSo8c7vfG9o7xXe3ha/r6rzKsUDaSMu1dmRjr+lsWHHIS+kEjfohg9QphwxTswF5T1t0/hNOC5VrzDHn26jii++o8XW1FmRl44gn6XXvy3uXK2DGwy8WBuWxC4CrWSxnunSfF8xEorLipoENWbytpd+4ebsCLYIr3WETz6UX67pNcMr2d5/E8sU61aIVfXdNgKq7pE8IG10f2Azw+1EJVt9/mFbwWM8/XCXExK9qRd2WpaItq3f48zO+d/yxAM0mOq0mMTvOcJ3+qSkRoTfjvU2bWmH5dUhNgNdJkv+YBKTV7l0nWQ7e5izOeiIAJW5khI/js8dgOYfL/OLscZ4SkvkwBXTTVdy9Jim5OMMN7hbdYXW+P0uiAF/zuIqB8v7FGZXTxRldWLLQcjjf96+J/x7qsG/EucqT+TxC1McUSSVPb/u48qZT8IL3L76J8rGcbcdJTjPti3feukeFYAHzAKGQQR89LgEFCL88voVvILZr6Fr/+W//1+kN+tb//39O73BgXf7nv/2H03P6Jx3I+XendzKcWHES/0rS5NR1tpQtssUvLiy5whSarCElIMG+mofHxUEfgysf9Qt0pDEfMnETKM8TbXtVaKWGrD2ysX/xWkz0TC96vZ7g26gTVGF4i0MviEKf9ojHiZ+TvJsBjrfYvyhezmK9iqom+4aaOS4iIzP6WkCj5o4ZWA94eJMksXhdg5PIlzz2gIOu8qZIy87JYolWC9IhaQoCA/sQz52Ulix7HOGf3vz0Y4/60S0k2OPHw3V6rO52WxkygBqiFM+LWGp365mexmDslu9q4JCxpS+KuohmVKq039Au77IE9OduH7dG7p9a+89Z9K24XwecB4wZgrtseXQfQLtnfYf262OWjju62KMINN6437H2pacQkOLwlxd/e3X47ulP74bz5K/zgyfPHx++8p/+8vr56Mj90/zxq5vXz18Nn/z63fPBH5PDl4/nT3/98OrJ+XlBir7GgJR4R8UM7eUDzH3yz98+Lf8xfOnKBQC52/dc+OUiYQc+DMGF2S+P3ELK5d0+njZAcgAKubRr0q8AZbmAg6n0qAGkgo+0D92DkprA59s6Wk6VlnNq9c20+pSWdsYJCWKyeu8RrZQQT3lDEtKF2RsSuzg0uSPnqH/i9AdDZ3jUxzxvjSWasnj0FbKZ27df3FVNMWDEO3SdwdHgeHjiOMejEw5Br7Cuoymec8Y6XJ4csVMEh/SdWfdwwL4OXPr1aAQ5R/iPfj2GjyN8MZd9PTkGYAef9mPP8Tr4/RB4cF32+q6LCSN8P3LIEvqQ0HcOIeGEvXM7wPMMWEL/kEE4fTzRQP9jhRwh+gk+N+g4NMHFcw8jh6ZiwmiILGNJxxTjED/2D4vvSOxkUIBjiUdY6SP6ku4hUBsgRdedTFABlR3/IKIRU++6wDnTLNHm/LHAfYFD1Qs7zT4GdhrBLlUQegkJ7XOgcqXSls9/oWaKBKGo28qAz1sKcSuFDNVChlsLmWyv67/GFozjKhzL3Ew28tDKh1H4CJNmZaKuzOqaYdI0hf/dek73HJ1az1792RImwjfzfGwuhTGAUzc1VWFIZxGJU7QzyL4VBmhBpVfFSN8/Ctyp4w6JPwMtPz6eeiej6dQ9PBz2nVGfHHP24H+kd8He+gE7YIEuyJXC7SkYPq2eYp+dXntZ64JZKj2T5UZxTqmRRwK8MaFNJ+EI45BfevQHJjKoHS39tzEeLV78LkakIcsoC1OrAnjkLTPMpLaoQTE0Gl8a7dH/oXZrozzqFfo3NnOp+FqXl07veNA/dAeuRZ9UOO4fjo6P8AHZXr8/ckfHPB07xfDokH4+cY9hIhqVz9QCkVG/D53JosCjo+FgMDxiiMP+cOAMKEE21x3SdDEzTmCQZ/4g1VzeSO0amXxcl58e+dNj5ximW3c66DvTY98dBkfTKQlOPKiTV3b5/2Wu2rDTrPAO1XdHqDtXdmdiUUUffvbPPvtnn/0z4TfV/zzUBzP7XuhyuY0ul9lNuofL9ekcJzpqFI6Ss4NnVPGEnM+uz/8e1+ezV/IwE8Wf+mQQeMGhOxsMyOGRd+QdDqbT6fEJfB76I6OJUrUhCmOhBaYZmlvyP9At1dgy8U8Z/+gpkV088X1Ab70rR3FChqMpOeoH0MkGAZlOB37/JDg88cAmGwb9Ezoe7uyoycBbTTwZeKuwJ2M+cyd+CpO1ce4W0yvKigNxcG1C3e8V4rzC7P0K6rMooSu+4nvPxwRaDH3AYlzmpATUyCd/DfPrloJeEJ2l3pwfJ9OW0Z7zry84BNZBQCtLMYIw5htOohaNq51EZU9jiqNiBWX4yhfwnt5+D8QFNt4RP2aPqiar1CfP6R0MNTL8EnvLvnVgaej8hhNZLAW13ixMM1E2rRkglLmlLbNRW8Io5G2my38Bn7Ytmw==</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_b3927818e3204be690b98b3be6c07212\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" 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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_d5660a98b46e49b8a1073c1b8504eadb\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -164,8 +143,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "counter.count.value = Array(0, dtype=int32, weak_type=True)\n",
-      "counter.count.value = Array(1, dtype=int32, weak_type=True)\n"
+      "counter.count[...] = Array(0, dtype=int32, weak_type=True)\n",
+      "counter.count[...] = Array(1, dtype=int32, weak_type=True)\n"
      ]
     }
    ],
@@ -177,12 +156,12 @@
     "    self.count = Count(jnp.array(0))\n",
     "\n",
     "  def __call__(self):\n",
-    "    self.count.value += 1\n",
+    "    self.count[...] += 1\n",
     "\n",
     "counter = Counter()\n",
-    "print(f'{counter.count.value = }')\n",
+    "print(f'{counter.count[...] = }')\n",
     "counter()\n",
-    "print(f'{counter.count.value = }')"
+    "print(f'{counter.count[...] = }')"
    ]
   },
   {
@@ -212,7 +191,7 @@
     {
      "data": {
       "text/html": [
-       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_e95948e87fda4160991d22c7d75fe4e1\" style=\"display:block\"></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_e95948e87fda4160991d22c7d75fe4e1\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" 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oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_5111ac75504b44448a5dd31dbaea5a09\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -224,7 +203,7 @@
     {
      "data": {
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FhdxuRw8Jiw2DBNtxTc6m/Mg2Zgtx4ipmD+Ynnt5wE4qmEbcKUsycr6sN8HzIed7SVeP+vEYUxrNovbJ9/lBgDf6COw7Abe6v7Q5nu/vlneG19WW6J4acANkfZk0llpnixoQS0b+eg0gyaww3N9QulW99LaPWDGxDO9lyjAWCt9zjzduycJrX75kBSTbAvyhIzjneVP008b2BS92oRLhRCWVDUfua1dtZtjoCTzhxHR/g26rO5S0MhrP1MnxIqEF1orihCe+iZ/Tw0QV/UGdRpdS+tUSLzGnhTVwd0sKYU/q2IAjzoOF0Bxjvz+QTlFxYlwzA4FNKI7Bw3qIksb7Lewj8QPz9/IF6R+aQuRDVfMKZI1gD6dikSnsqbir4m/nwbBirpn4FH6ueIBidU3xo6RO3fsKsAgpbpraCC4+cgwD4xHA+KuV1XFe6ItC4K1jDbABQ/kbWb5Rt5S9/qbJb6DXsZOcjCNLccocd7g9q63aCjgo62PxIjanw0o8DdJDM4pzXvfsaVcwiKnQL7Jt1rnFLAwenVnYOASsMIhG2VasFg01U32pE4PnY2+UMNbxcXFuHlV5F2dSrIVZTkp3I++ZC3qFrCtzJtLDMgEq3RWi9+fgtC8/S6xDsrfP2peNbabiVdGe3FEfjfgSptFpZ/LyhPKjv2hDGget2tb2PE0X8nzkqi4eo9d3Kp2nV11ZtvZ6vrVic16KuXs/TVeHleoGeXs/T0lYdvW7U0et2HcNCwhraLKXePORG9VxaXQTz9VpWyut5SvmgtYS2yVr81TCE9ydRjH5lTom2Mwcwy9PkHLUs5rdRPvCnGDj7MPNTtBD6eUJMrc4FXuDtfNUp9sHciY1VtmnPCV1IL4+KbWKTbKNh9FsEU+Z+z+ugdqz87W/YTMV7E+ZgcONqG0rblNo0j2qt86i2mkdX8+iceXTvy82jD5abPLWWyVNbTZ5/6slz7wtMnuR/PhhW3aiCciFO0Y3RVfEs7lfiMvCE3hTk6LGl7IKFtkhbRemOUTK85WRxZIyP3NUWaeWIYCkJujVhgJe18H0HN23HHIifzN1IRIIPJABTTGT4lg9tp+GEPY9104IlCgTvc8bX12B89ohT9/iOXMX4q3uUjgHygTjc7glTFBfd5rAabsPhY5nzrbDi4Fw2m+RczPuLRVaKxWFKpIr/QwddKpyiMN7EkDiht3PH8I2AdCtGUbC243x231TtLoVIjN9U0sfj/B51ljY36xWft7hUwhBmlcXDPAvqJeFsMstEeHL7U4lD3ng8uX7V67cFvZ3acdckDaVrvHjELcZ3bZt+dQ8ZJrDesOZTdBScz/oHRdqrFovE4bJBrfnx9+urCPQ8XoTw2qgiN/dXkZulVGShvSrrCI8wV0nqNbyfkgiIfx4lKe5Zk+OjG0pjmHODsVMFLY/nLlBWV2LieD53T1C1BQuHLuPwABzOsHUZMIwuOz3xLsUoJlS5dTl5RYUWfHf6hLKARlf8igW+ijS5wLNcjexwF6N2dhpBy5XIJWDx1cCPyc3AxJsv7gZugc5TP87wZvNXaTSmAYA8mcIYMGqjDxPXIE2mKM1vup3owh+jzRRh7Y/iMb7ihey5ASGFnd4SBDY3iwtINz8myQUmoC2JiG8b2yQXIWdgmRBMc3rdKcRNm0MU9e+BPwm6l37alcrFVvM3n/hl4tvpdXEqkKdUtsRypCh4C63ielpykADLDMyDTqEsFH/pRuLBBTmRe5KxcJpY7dQXn2Whszt+f4Kyylp/86k28N+SCxz7FrrA4yzUt6hwC723yZQjd70UudouhsnkJ3+IJvzmDraqRNIH0tEHNrfg9fcL6BcEhoTUyNL5BL8K6+ckpdAgfPnVG9yRsPSnXDyNh6I3JT8it21jOLWvQQ24O5MbsbAO06uPuKGoQ0SDr23TyLJsV+3beHCtN2IPaxzNELtErziOcvv19wyR+kinquePmk2S4HtEcWVzo8gmvDq2d0LmtXD9T24v1lKUkKaq34K+ffMpwvpH1K8Zj40nXG0XcSKbrlWEsoE5HAU9oIN84USQjCZWMOw+voEdy4wbEZRGRWdaXnq0tSlLzC97GN1dxGfefvW9VP9klRJGzTTJgTweNjc9NUTjTiPthnGZaVSKh/3GYlJh/lha45o0dVO5v+bfWYPrhvVcFW5QTlEmS2jnA9GCBlX96d4qUaEv7F0c6BIqVIMeVqN/O1ChBfNgYOwv1oY6uL3bIQXVLZVyMyFaiZVY0EpJlfneX1G+7xDRQIGbjCnMJwK0zdVjA5+0wkmE9m2vbQcgCbw8Sq5R1m7Bf5aPUBXQDyZ+lv0UZXkfLBawdONRgkXJPsNQWk53CQ8xMHrL4dxtoswLoruK3zCmJMWXVhI41pepNv4sSYe7xVkqi9Ho/v4f8Tefqj5ye/S7tIEHfwCjxlpboeTLGVyvpMgsiqp06Mc0OlJusRWnLiUGQPeItWf716K4NQkgy9GUX9toFgUVJ0VZJLXOsdKR5ET15p5yosilnMh3UTpSZruYGECbmIrsVjExAFlMZXLdw8L2qqkGp/h2lYxNENUMMU++lGhddgEOnELOAYw3+EMl5HMV6DIXDPaaLACgD1Udo5wmVaEPSbfaAR+IK9Xt25bZFRMyJaF7c3Ghlvi/3OQw9vyAL27AAxGC4bLQjY0mocj96j7I9UEXR3EmQOELDrmMYjX+FCl9spGuU9ofF/RAXKd2dh1f9Q1DLT7Ijy+16VvkTL2GL6HhVwjEGryasmlc4L8sexnWE0KCHzhF4qVGlfeG8+dlsDlVHwiWHAYq5MaBgMumPb2jNeSQTt7RG7JY9+748U09s6hVw4EqngTK9/M8jYYwmXc7pIU3+KaVAnGjBH9p68sF+BjFxplbApnv1BdQxWj+9wR/PGtTJsKyKwE053fWiT9PTiaPkl49jlF+pqm1g9WNsKUEUhCmEnkJAxWuZ5Ha2ZF5kCbyL8cDHozqPOD7sCSgu4Xr6u6HRGjpaF2dErPAyo84fcE4dEGzzcQsAcTwHHfN+vUL8l03mDikkQ4HgPDGKRLrx+0F89Aj/EmvKB4fTCJg5rVwKJhb8onBLXpdiOubTwUl5qpslqRJ7AUE9XvjilARA+Bs/padIKWJW4TpOBxxiYeB9ImPIDlQJdcN8HystdlFwtLmD2rjWhA7l8lQIrVAmHQJp8QvtG+vEh1NkS7fWsSuZOeL3NNARZcEKXoKycLB9+6m2rew/aX1dXQhXRLxOTVURD1h8duaslRCYNqCD04Fp/w9ew17Ue5NWhUDJ/KuEkHPR7NJUfmC9jxNrvzbRXrMiJYIeDvPQnly3Iinpkb5dvXljgKKVHmjKIy8CR/kyJNpExYkV0jwIuAQuW5Xp2dLLJJR4ZFXAbP4qmQdleZUuPSdQ75t+D7LvGUHTg9I6HdTEUTCD0fzFhxq4S76QUwSgSsLwEsrHH14FeJbLfS5oVOUID90pmw1aBGxcgWBp8aCOBw5mlLRu5VOpeOJCahh84VsGcqqfS1Ua2dTmGYQzn+cJhdvmGXafIxQ2B2AbzsJB9f0+gy2Hk5Su1dRHCZX/RBdgodBSiVAwuX8LTD4a1fC5h65GE3Z4ovCr4uKE7//My0B9sOzWZZf0BigVFAr1fYv7gRZNrh+S9w+8pGSNKPn+buyDS3IgceS6i4S1OTqYGwB/XupEj3lW+6TGru1C04+b0m3+QrMO9H0Z3nSaWkmsmaJ9ZCvIb8J7fvGlmzrVAsWnKGPkQK5i6t4C0tYQqP8Xfl5cMrprdxj2KYUFEbiRcNMrS4w+guc2/29myISUCZfKf3mU4vi3YZTPARJYZEJc+tr5Su0bHZrjBQCCE6J07hRonOzUdN4wM9WUsW5uEXxQAuuBx7aSr2tOZHtQQuxknMGt/ICuUlI15k58vQ2J0guQOfX+YHQclmQJpPJI8Em+0Qm3aYS8OcMCQcbyhCd+pcR/pBrB1+x5Md551Yug3ekSTnP4jz5JUJX3U8N6EBzkgTnkBIjH5SoIngrHnqvy/MimWVEM7BM5RhacfkR3mcKkuM3nOLQFq3cbxtK9fJeGNMKzIYT0KU7Srsl+1gvCfwmMeJukJS8uDbAtsugy9OQJ5fSNYnl5qOyZssUSgTdzh5dixas9AXuJtk0Sw5Et6yh1crIJetlcRHkwMfiInBziAtizTdPCmW3C3jOlTm9nSUUQRY1F5wA0wggbDkPW0woLBbKSspUNWnyekmgGflpESWQsFl6Ezq5UqzqDtDTeT72FFX57jtBZDzwugRMXS+OY9FBlKTFtj50hPtuJRjmHUue43JLx2K1iqpLutPgvDWy2cjBUkWvtxXdVllBIrctLfW+bKmnhXM+p6nel01VQvNt9bTBm5d4I/12flsV68Wf2VjvP6ex6sPLHdrq/R3aqloc77QeAVlq+kqoRSDPXktOMUtNMAsZIXG6RXPo6+KowT1n0gK/Pp3WT8VggPtPjifVrEgL3ZFvVD25rN8AO5+L5ps9ygCoOPN0DnAGCrdxsFzSxgX3IcuVqV130vSz3GSliF/Maj5SJJ6oWiSTloNYNTT5uwDNN4QspatXpwhNmnS1GB/9SQ7l1gNbIZrk/nvsYlDcXp+mVFxhbHa9/CEa+aA4cki0+hZ1zWjv4W8vVPmUtvTdm7mWPi+TOiDe1y6xy1wDmDUw3E/A92RJl728n45HFAjtyAeh6xztKeai2m3uKqasf0KpD1vYxbfZ1K6zEdkVXtfFE4xNR/1a6/FQ2VxYkfVFFdlrq0gU36kim4sr0rCYyZNY3geuP9xv6aXuVH6SI7mkYwYE9z0f98MhXGVL0uHvpTGvK0lIgBbiuDTYzBX2m7zc8yVLa/Jlq/ZquK526mdZdIm26QdcboU1saZdFPMasDGAUbuwlgaqXsXokH2258tdVyveLsRfNNqw+ZUGLxhQkdIAV4aLeVCauCNez1S/Qm/5S/Rq1+iJl+DVr82r5VMWcnm3WPvFePV7kcgHJeh3G9jkj++aAC3BWyK2uQ8tgeawWVc6GVseLLrHLtLWIzxa4E87bVDV6Z25YBck4kjbvKP2HQtdtMJyFmsU49s7NkXPubau0HzUpBkYG8ejCTkM3LmkKt0KO/+0UYPEWs9RyJC4VqVn3tH7Vmep+O6i8HVbnWc5LpA00vRaOrLxYN7ZsPKQl9AJ5uoHD9KmHDxMywbkB9Ki81s8LdSuMcc5P0xqvviSGt9W64IsbxyxJLnuOr93uQbWePhFwUF53AXA1SyXMzWyzwtmoqKyxU0Dt+ji91b6czdv16CL4MqmNY/nyovVtHlwwvb3AMX8xTr1ogV915oARXdJnhBuZX2gM8CfRyVoff9lWsFiPf9ylSgmflEr2rYslW1Zv8OfnfH9xD4WINlE2/Ukasc1XKe/3ZSIoW93Htz2iBWWn0bEBHidJPnLJETdXv80yXLwNkdx1i8CUMWNjPC483ALLOdomu893MpThLIApoDNdBZvnqIU7T3EG9wVssNqd22UTEL8NY+TGCiv7T0kctp7SBaWFGw57K4Fpyg4hzqsNeKc5Ml4PMGoWwRJJE9u+zjxh0Pwgtf2vpvkO3x2J05yktnZO/Ov+0QICjAPEAIZ7KPHFWABwi6P7+obimb3Wggr//2f/0ftW5ryX/9f7RuGcvTf//n/1L6qWt4GZP1fte85x0qcxB9Rmmwb+oLSi+ziFxMXX2UCja4hJUThmpiHD4yDRoYnAdYw0JK5+ZCJt4GyvKJ1T0q9lJClz2ys7b0upnqqGf1+v+C7USuIyrA2h34wiQLSJ7aSIEf5ZgY4/sXaXvntLNqviHLSN6ybO2VsZES+FzBXd3coWB94eJMkcfF9DUYin7LoAx52ha+KdDs5uphiuwXTQWkKAgMLEZ88qWxZ+nmE529evewTT7qLCfbZAXGZHq17pycMGkANo5QfGFHEDtdv+jgGZbf6sgYeNBb0xqIuRTMKVVqb0y5nWQL682kNb45c21bWDmn8rbxhB9wHHDUEh1nxyU6AXl95ii3YLZqO93TRzyKQiOPahrLGfQwBU7Q+PP5tYJw9enVmjZNfx+v7h1vGIHj04fWhY2tvx1uDy9eHA2v/49ND80livNgaP/p4NXixn9ycGS8uxurWs5fO+uDtWHty6F4lv73b+iUOrgfaFL16611ZH56tvxw8ej1Qr/znh646Td6v68+9HwbZm/Hrs61nz7VsK376/PXAeT2MP9rmY21/Szv8+8fB1cFPV2d5lr3JtsLoF2NgHfzgnam7JfvkGxCYezY84Azpews4d//nHx4t+Y/Q5i6BAPRPa74GvzRcqIofbPCq1qpTwJB09GkNH4DAZQEs5JKxgrwClKJhJJxMjj9AMvhta9BhKbFjeLlpo6bWqanbit5MTCe0pINXmCBOFi9jIvUqpFdd24TpgkkBiZswWtqWqVmG5VqmYXg2zvKvcYENOSwgDLnUE10rr88mCHgMNmzPVXXTMT3HwiPyWnmtdgvN4gvTuAZHOAUfbDDIp281w6SvpkZebQdybPyPvLrw6OCP+NJXzwVgFX9tkH4hWMXvBrCgafSDwBpOcPAnLS2aoEOCrhqQ4NFP75r4iAUuQTcohKrjQxbkP1qIjdE9/AVEVSUJGj6K4agkFSc4FmYZl+QSDAM/6kb5jol5ZgmOS7RxpW3ycV8DqJmYoqYdH2MFFA4hgIgcqvttsXyqV0WLs+8XrhU4RLlwj1rDsaa5YEciCLkXhXRIULhKZasvkmG9LBIKNV1UBjwvKESrFaLZYimkm84v5nhxbf8jVmBqEeFo5u3xLT/as5EdHmEer9kONUNDspbmWRX/UA7JRqht5WDwTlEZse/G+U5zKZQBbE0Q+xlmGRom2camD1pTohCbdelJOfkMTcPzkaqPrJFu+mHg2vpQdd1wGKi67rk+Y+8rGZMKAUPhMkZlQ1aQXGD37GSeKUbyAHziTzOcSWzThjaRaPy10T79X2vHzpVIuzZ9ZbOXCLB7dKT2XVM3NFNTyEcWXN1wXBt/Urav647muCxdVx3dsg3y7GkuzANOFTcHx1jXVc1SCKwDE4pp2RTP0i1TNQk9z4JR2TZIuqp7hqaaNk9D9VxHUy0T51sWsGToNi3bNE1H00i6B5R1ldHzTE91FZ6G7RnAtq5UH9U9wsyZKgbGSAbwbpsGIeDCjOFoJinENjVVt2mB8NtzbcflCWseVNBmFXQs11NpBV3LVh3LIXgu0LYsypyhAdu2JTDnarpra55CmQe1sgyCV0zShA8QkcPKYTOzICRoCMNRVeMY5ibyYaxdptm9FjX6vCFKH/rDkaqaaGhCOzogRG2oQhsMdTUwXN/4ukPUv9DfhVlt/iihkkFCLcYINkCoxwoZHDR75eSunNyVk1s4gl/lZ/cLOaS7zc4n8TnnupzNTuKdXM4v5ziSQaj0FNUlfMOaK6iunL8/jvO38svua/S4qm1rrofUwB6aoev6vuY7VhhYqh0OdcNrNHrqVklpfnTBBsX23H3/gSbSRRTB3muq8J1r6hgITHJvBHU1PTv0Pc8FH9Ry7dA2wpG58kC/nGV5jm5w6aP0Brdct826/AM4ld1e4ZuIde4p2GgAfYRRcvvBkeLprufCwGvA/AJujguuj3f8VdwYX3WNkQHeizP0TR+cRmTroavqyPBD3RoFd+7RWlHDGfkI8hfpjCM99MEfDcORpZq+anqGq4VoaGkgmdAZeX9iX4tGZMBP1sqYjGYULpfmVUGZld+18rtWflfl7zz+cX/wPPhhenj5bnKQue///tF6pZ8/nbzNvKvX79xfnp26gyfW/vO3N5dbz/fd9Rtv9Ora8x+/vTHfPH3vTl/9+uHVzPrp7ccrLXuuupfvrh4PVn6XvGAHQ5BueqqtaboOE5nNL9k15S1ctNMM1XBN1fVM3dIB1627ag1kV67bynX7k7tuvqM6pjY0oVPYZmCbbujohjay9cAKjJHqrhyaP4b59m+9poYrZdk2W1KDasNfkyydqZpqOrrF0h1bcyydPpuW6SrVahFO8jRTNTSKZpmawRaLNFV1bY09O7bn2hRGNUzVshyJhGY5OiMBxDxvg5tfaLpuW7rhbnBzikDC1cBvcv45q00InEDVNPzAd0ampnvuEByg0Aa27eFwpKt/Xg/I84TlJq9ckF75PivfZ+X7NPk+06ePvIH9+mr0dv08eKpufZH3q5XvI29W9FTy44EX4piGzu9WrGct8HwaMKTNinWAldez8nr+5F5PiDwf+oPnu7pu2u7IV4dgPuEYd+D5jrZaxvlfb7L9m/s71K+49/+Vv9EO9M/xQILQCkeupqumhsyRHXqO4elDxzRHrqGp4Z92v5sm7HfT2H43De93W7kfK/dj5X40bHk72B9c7e+PD939/Wf7W1/mfeV+CO6Hxu150xbvedNqe9601Z63lQuxciHCE1XzvNCz1dBXVVN3Xc8B5TUs17csY6S76O573rQ+/v7Dff99vT1vRuCpw2EQ2NAPTfCUfN12kGUPLXek64bnr440rI40rOy7lX23OtKwOtKwMu9W5t0fwrxDpo3CMLRt27DMwHJ8FXm6HvjayNZULVT/OEca/DCAkVFFaGjaZqh6vjEKfTvUhq5re7al/5HNO8Wab+CZLrHwdK8w8TSrISjtqitDb2XorQw9ZmCdPw7CwWk0fnXmXr0Nxt7jH57Gg9mBl781zBvjfP00tZ4PtOhFeLj1bnZzvvVof/jjYDy+QB/tq9hPvNET6/Hg42n24dB5/+Ltz+tvXl1PB1fwcuhF5iN1y3s2Dge6/fjlR3f/N+OZe/rD+0cD63D886GZuNrPW/pLbThwDz7YH63xo3fPvGcv0ZNBsn/w6KP7zsqC9a3njjaIx9OfDr3z8x+DdfvJL9rg9Oz8t7emuvV3d/1dMr0eXDnnL89c87nprruHV97g5mc9OrN/js2fPfXHq+eDrffPhmdbVz9MX6xnB8NfB79a6ZO36+js5/db6TNVHdx8HIdvt7Kry2dQ3qPB4Obvr/fPtt5PXHXdP/gQD6b7unmov9/68Gx9/+njHwfv9m8+frzZf+Lurz9+FeoD+zS3PjquPlLXrx6PTgfR2XDw0Tl49NjdOnt8qA/Ofr5+cubuz/wX7rOz5+cD9+zixaE5Np0XXjywXg6cqw8/fXTfv7zItl48jbPBdLw+euuZs7c/bz2ZXqPBx9ePrg7Nr3lB1ef+W+KCK+se91tZzYa5dZ/brRZY+V9uy4jrgQkMhoirO66revyWkXrWIpNfMx3P0CzHchxLgzm/vle+gejKJfjzugT3ueTKEkuxVn7Hcta4ObQ8Uw30wPFcM9B930LhyDdHRhjgs8dotTPlj+AI/LvfcIUvUbJMg2x4d3RPVS36rHqOq+KbrODZtDTbdtlGeNd1TE21hVukdNPwTF2jRGAasQ2T3eRk6bZN9/SDe63qOr3iCqBhSsKzgEAEgHXdoaUYhupaLr1SStNhlqCIuutZqk0K0hzLc2A+EYh4muHYGrt/CljRPY/eYQVzlQqY9NkygB69rwqfIPYc8VIsNm8SYEs1PVfX6e1ermvgwwuEuKHBC+UWGHUtwxWJmLaqu4xt1XQM2zU1eoOWYdiqSk4eAL7hsluvDE81dUc3RCLAm87kYDu6pTr0Oi0DIB2DClY3PNu0LXovGTQUpiMQMTTddFybIHqmgQ9NW7Tupqk5tLl1x7M8m3KL9/XAfCdWR7c9zzTZDWW2CSYF1Q0QZSkrTwdRmVRWUHcdZCLqiaaalm3R27wsD8AtWgVLtyCV6gw0k+7ajklv+QIEzTUlmZiayRTM1VTQNpXei6Z60MIOSTfxsRCv0Bnb9CxTkTjxQLaqw+49M0x2sRu0tglWB1VC3dI9nZ5CMRxLVz2piUHups1aAZRXBY2lz54FfYdxAqzoOrvxDcZ1fEubQAR0RMOhJwKM62NQpQL1MTSDCVzzPNeyidxcUHHXw8dO+OqAOpRXpoHeGxbVB1c1cVyLdkAXama5tGMCK56p2ZLGgkmkU6EVdiFrEdxQVOC2Z9keFT7IxPQcMPaaI1ZfZROaFQRW6NneyHFN0xyGnunZyHN8A9mObYajP2wIy/qsBUprFbZaha1WYavF65P3DZN82TXFO0QuViuKq/DB1wkfrPz6e/r1RmgjV3WQjRzT8nTwDfBq21BDjqGikf0l1hNrO/5blwc/e0oEbYCJ7lmIp72jahT3jFBVnZGGhoFn2qOhp41UF1maYTn2cOg4ZDxc+jZvHnjhvbo88ML76ATgRVe68cAL78XigRfeTyVQXnQRAw+88Nw3D7zwsBMPvPA8Bw+8cA8kD7xwG6FQwUXL74LoFq1g88ALA2w88ELHQqC8qIsf7zB7MQlSMBEbLcbCqMM9lAExcMmMW+uXnfgEZ6/VUA8mCflCXvHeD3ACKQbP3NjiK3JSBINXgH6N8tOugF4SHaX++IJawdJnxw7Z62MGgetQQAufrioI43ywxrr467wR+TYw/HpYDSnsi2CQur7eAwsZw9FMAC4pwyv74Nmjm2dAvMA+io6hAIyTJbM0QIcwRrfK8K94jF5T1hUJnb4KYimp9UdRmhVlk5oBQpVbWdC3Yks0CnmRwfw/AykzSw==</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_e95948e87fda4160991d22c7d75fe4e1\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" 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RdS7QyrCyXVTI7Xb0kJDYMEmwE9fkbsqP7GC24CeufPagfuLlDXehqBpxuyDFyvm6OgDPu5znbV01ns9rLMJoFrVXds4fKqzBX3DXAbjD/bXT4ex0v3wyvLa/TM/EkBsg+8OsqcYyUzyYUBb0r+cUJJk1gps7apfyt76XUesGdqCdHDnGDMFH7vHhbZk5zfv3TIEkB+BfFCjnXG+qfpro3sC1blQs3KiYsqGofc3q7SzbHIEmnLiOL/BtVffyFjrD2X4ZviTUIDpR3NCFd5Ezevnogr+os6hRat9aokfm9PAmbg7pYUwpfVvghHnQcLsDlPdn8g1Kzq1LJmCwKaUZWLhvUaJY3+UtBH4i/n7+RL0jU8hMiGo94dQRLIF0blKlMxU3FfzNfHg2jVVLv4KvVU8QzM4pvrT0ids/YVoBhS1TW8GFR85AgPJEcT4q+XVcF7rC0bgraMNsAlD+RvZvlG3lL3+pslvwNZxk5z0I0tpyhxPuD2r7doKMCjLY/EiVqfDSjwN0kMzinJe9+ypVTCMqZAv0m3Wuc0sFB6dWeg4BKxQiEbZVqgWFTRTfakbg6djb5RQ1vF1c24eVXkXe1JshNlPinUj75kLaYWgK1Mm4MM8AS7eFab355Vs2nqXXIehb5+1bx7fSdCvJzm7JjsbzCFJttbr4dUN5UD+1IcwD1+1iex8jitg/c0QWT1Hru5VN0yqvrdJ6PV9aMTuvRVm9nierwsv1Ajm9nielrTJ63Sij1+0yhpmEJbSZS715hRvFc2lxEdTXa1kor+cJ5YPWGtoWa/FXwxTen0Qx+pUZJdrOHMAsT5Nz1LKZ34b5wJ9i4OzDzE/RQujnCVG1Ohd4g7fzVZfYB3MXNtbYpjMndCO9vCq2iVWyjYbZbxFMmfs9L4PasfK3v2E1FZ9NmFOCm1fbirQtqU3rqNa6jmqrdXS1js5ZR/e+3Dr6YLnFU2tZPLXV4vmnXjz3vsDiSf7nnWFVRBWUC36KboyuimfxvBKXgRf0JidHj21lFyS0edoqTHf0kuEjJ4s9Y7znrrZJK3sES07QowkDvK2F4x3ctF1zIHYyF5GIOB+IA6ZYyHCUD22n4YY9X+qmpZTIEHzOGYevweXZI07d4wdy5eOv4igdA+QDcbrdE5YozrvNlWqIhsP7MudrYcXFuWw2yTmf9xfzrBSbwxRJ5f+HAbqUO0VhtIkucYJv547uG6HQrehFwdKO81m8qVoshUj031Tcx/P8HjWWNjfrDZ+3uVTCEGKVxdM8c+ol4Wwyy0R4Ev2pLEPe+HJy+6rXbwt8O7XrrkkaSmG8+IJbjO7aMf0qDhlGsN6w51MMFJzPxgcttFdtFonTZYNY8/Pv1xcRGHk8C+G1UURu7i8iN0uJyEJ9VZYRvsBcIam38H5CIhT88whJEWdN9o9uKI1uzg1GTuW0PJ67QVmFxMT+fC5OUHUEC7su4/AADM6wdRswjC47PTGWYhQTrNy+nLyjQiu+O36CWShGd/yKDb4KNQngWe5GdrjAqJ2dRtByJ3IJWBwa+DGJDEys+SI2cAt0nvpxhg+bv0qjMXUA5MkU5oBRG35YuAZpMkVpftPtRBf+GG2mCEt/FI9xiBdy5gaYFHZ6SyDY3CwCkG5+TJILjEBbsiCONrZJAiFnoJmQkub0ulOwm3aHyOrfA38SdC/9tCvVi7Xmbz7x28S30+viViCPqeyJ5VBR8BZcRXhacpEA8wzUg04hLLT80p3Egwt8InGSMXOaSO3UN59lprMYvz9BXWWrv/lUm/hvSQDHvoUu8DwL7S0a3ILvbTLl0F0vha52imEy+ckfogl/uIPtKpH0gXT1ga0teP/9AsYFgSEuNbJ1PsGvwv45SSkkCAe/eoMHEub+lPOn8VA0UvIjEm0bw6l9DVrAxUxuLIVlmIY+4qaiDmENDtumkW3Zrtq38eRa78QeljiaIQ6JXnEd5fbrnxki7ZFuVc+fNZs4wY+IImRzI8smvDi2D0JmtXDjT+4v1lMUkaaq34K8ffMpwvJHxK+5HJtPuNYuokRWXSsPZQNx2At6QCf5woggGU2kYNh9HIEd84ybEZRGQWdSXlq0tSVLzC9HGD1dxGfefvWzVP9kkRJmzTTJAT2eNjc9NUTjTiPuhnmZSVSKp/3GalJh/Vha4pokdVO5v+TfWYLrivVcEW4QTpEnS0jnA1GDBlH96d4iURVfOLo40CVEqAY9rGb/dqBCCubBwNxf7A11cH+3QwqiWwrlZkKkEguxIJWSKPOjv8J83ymiAQO3GFOYTwRom2vHBr5phZMI7tte2wlA4nh5lFyjrF2D/ywboaqgH0z8LPspyvI+aCyg6cajBLOSfYah1Jzu4h5iYDTK4dxjoswKoqeK3zCiJMGXdhI40pdpNv4sSYeL4izVxXB0f/+P+JtP1Ri5PfpdOsCDP4BRI62tUvLlDG5U0sLMi6p06Mc0OlJucRSnziUGQM+ItWf71yK7NQkgy9GU39toZgVlJy2yiGudY6Uj8YnKzT35RAuXfCLfRelIme1sYgBtbCqyW9nEAGQ2lcl1Cwvrq6YanOLoKhlbIKoVYh5/KdI67wLsOIWcA5hv8IdKyOcq0GUuKOw1XgBAH5o6RjlNqlwfkmy1Az4Qd6rbjy2zEBMyJmF4c36hFv+/3OUw9/yAAzfgiQjBdFnIxkYTU+RxdZ/C9UkXe3EmgOELTrkMYzX/FCl9cpCuU+ofF/RCXKd2dx2H+oapFl/kx0Ft+ha5U6/hIDT8DoHYgldTtowL9Jd1L0N6QlDwE6eIvJSoMm44f18Gq1P1iWDJaaAq3DgRcNl0pHe0hhwyyDt6QxYb3h0/vqlnFq1quFDFo0D5fp6n0RAW826H9PAG37WSI26U4C9tfTkHH8PYuHJLIPON+gKqmM3/nuCPZ23KSFh2xYDm/M46sefJzeRR0qv7McrPNLUOsLoSthRDCsSUIy9hosLtLFI7OzIN0kL+5WjAk1GdBhwPSwK6m7uubn5IiJb21tUxMQ2s/IjTF/RDFzjbVMwSQHTPcWHWr1+Q77rBwiHNdNgBhA9OEV8/7i9Yhx7hT3pF8fhgEgExr4VLwdyWTwxm0euCXd98KjAxU2WzRE18L8Co3xt3hAofAKfzt5wEKVXcwk3HlRG3eBhIn9gIkgFVUt0Az/tam00kzG3+ojZuBdFzGQ8lVAuYSbdwyvKF9O1VrKMpUvCtReRKer5IPXVUdImToqeQLOx8726qfQvrX1pfRxdSkIjPaaEiygnz39aEpWICkxZ8cSo45ePsNZxFuTdqVXScyKdKBDkfzSZF4wvc8yS5sm8XyTFDWhbAx3kW8pOjRrw1Ncq3qy93FFCkyRtFZeRN+CBHnkybSkFyVQhehDKEr9vV7dmyFMmoypFXoWTxVcl6UZpTlaXvXOHbhu+zzNt24OSAuH43FYEl/HQ0b8Oh5u6iH8QkHriyAry1wuGHV8G/1YKfmzpFDvJTZ8p2gxYhK3cQeGzMicOhoykVvlvpVjpemAAbVl/IkaGsOtdCpXY2hWUG4fzHaXLxhmmmzdcIhdMBONpJOLim4TPYfjhJ7V5FcZhc9UN0CRYGqZUACcH5W2Dw166Ewz1yNZqyxVeFXxdVJ37/Z1oC7Idnsyy/oD5AqaJWrO1f3AmybHD9lph95CMlaUbv83dlHVrgA19KaruIUJObg0sLxb+XGtFTvuU+qbFbC3DyeVu6zSEw74TTn+VJp6WbyJ4llkO+hfwhtO8be7JtUC3YcIYxRirkAlfxGpawhUbpu/Lz4JSTW3nEsEMpKIzEQMNMrC5w8Rc4t/t7N0XEoUy+UvrNpxbBuw2neAqS3CITZtbX6ldo3SxqjOQCCE6J0bhRFudWo6b5gF+tpIZzfovigVZcdzy01XpbMyLbnRZiI+dMbmUAuUlI95k59DSaEyQXoPPb/EDouSxIk8nkkaCTfSKLblMN+HOGhIINZYhO/csIf8i1g0Ms+XHeuZXr4A1pUs+zOE9+idBV91NDccA5SYJzSImRD0JUIbwVL73X+XmRzDIiGZinsg+tCH6Ez5kC5/gDp9i1RRv324ZSvbwX5rSiZMMN6NIcpcOSfayXOH6TGHERJCUrrg2wLRh0eRvy5FIKk1gePipbtkylhNHt5NG9aEFLX2BukkOz5EJ0yx5arY5c0l4WV0EufCyuAneHuCHWHHlSqLudwXNC5vR2lhAEmdWccwJUI4Cw5TysMaGw2CgrMVPRpMnrJYLmwk8LL4FUmqU3FSchxarhACOdp2NPUZXvvhNYxgOvS8DU9OIoFg1EiVvs6ENHiHcrwTDrWLIcl9s6FptVNF2SnQbjrZHMRgqWqnq9req2xgocuW3pqfdlTz0tjPM5XfW+7KoSmu+rpw3WvEQbGbfz+6rYL/7Mznr/OZ1Vn17u0Ffv79BX1eZ4p/UKyFLLV0I1Ann1WnKJWWqBWUgI8dMtWkNfF1cN7rmSFuXry2n9VgwGuP/ieFKtirTSHTmi6sllPQLsfCqaI3uUDlBx5ekc4AwUbmNnuSSNC+Ihy42phTtp+llusVLEL2Y1XykSb1Qt4knLRaxaMfm7AM0RQpaS1atThCZNslrMj/4kh3rrjq0QTXL/PTYxaNlen6ZUVOHSLLz8IRr5IDiyS7T6FnVNae/hby9U+RS39N2buZo+z5M6ID7XLpHLTANYNTDcT0D3ZEmTvYxPxxcUEO3IF6HrFO0p5qLWbe4qpix/Qq0PW8jF0Wxq4WxEcoXXdfEGY9NVv9Z2PFQ2FzZkfVFD9toaEsV3asjm4oY0bGbyKJa3gesP99t6qRuVn2RPLhmYASn7nvf7YReusiXJ8PfSnNeVOCRAC35c6mzmKvtN3u75krU12bJVfzWEq536WRZdom36AZdbYU+s6RTFvA5sdGDUAtZSR9WrGB2yz/Z8uXC1YnQhPtBow+FX6rxgQEVKA1zpLuZBaeKOGJ6pHkJv+SB6tTB6YhC8eti8Wj4lIZdPi7UHxqvHRSIflKDfbWCLP441AVKCj0Rscx9aAslhq650M7a8WHSPU6StV3i0wJ922qCq2ztzwS6Ix5H2eUftOxa6aIXlNNYoxtE7NkXLubav0HzVpBkYK8ejCbkM3LmkIt0KO/+2UQPHWu9RyJC4VaVl3tH7Vmcp/+4i93Vbm2c5rpB00vRaurLxYN7dsPKSlzAI5soHD9ImHDxMywHkB9Km81u8LNTCmOOcHyY1W3xJiW9rdYGWV45Yktx2nT+7XANrvPyiYKc8HgJgapbbmRo55wUrUdHYItLALbr4vRX/3MPbNejCubJpzaO5smI1bR6ccPw9QDEfWKdetSDvWhOgaC7JC8KtLA90BfjziARt779MKpiv518uEsXCL0pF25Glsi/rMfzZHd9P7GMBkk60XU+ielxDOP3tpkQMfbvz4LZHtLD8NCIqwOskyV8mIer2+qdJloO1OYqzfuGAKiIywuPOwy3QnKNpvvdwK08RygJYAjbTWbx5ilK09xAfcFfICavdtVEyCfHXPE5iwLy295Dwae8h2VhSsOawuxacouAc2rDWWOYkT8bjCS66RQqJ6Em0jxN/OAQreG3vu0m+w2d34iQnmZ29M/+6T5igAPEAIaDBNnpcARYgLHh8V99QNLvXglj57//8P2rf0pT/+v9q3zCUo//+z/+n9lXV8jYg6/+qfc85VuIk/ojSZNvQF9ReZBe/GLv4JhNodA0pIQrXxDx8YRwkMjwJsISBlMzNh0x8DJTlFb17UsqlVFj6zMba3utiqaeS0e/3C7obpYKIDOtzGAeTKCBjYisJcpRvZlDGv1jbK7+dRccVEU76hmVzp/SNjMj3AubK7g4F6wMNb5IkLr6vwVDkU+Z9wNOu8FWRbidHF1Ost2A8KE2BYaAh4psnlS5LP4/w/M2rl31iSXcxwj67IC7jo23v9IRJA7DhIuUHRhRxwPWbPo5Bya2+rIEnjQWjsWhL0Y1Ck9bm9MtZloD8fFrDhyPXtpW1Q+p/KyPsgPmAvYZgMCs+OQnQ6ytPsQa7RdPxmS76WQTicVzbUNa4jyFgjNaHx78NjLNHr86scfLreH3/cMsYBI8+vD50bO3teGtw+fpwYO1/fHpoPkmMF1vjRx+vBi/2k5sz48XFWN169tJZH7wda08O3avkt3dbv8TB9UCboldvvSvrw7P1l4NHrwfqlf/80FWnyft1/bn3wyB7M359tvXsuZZtxU+fvx44r4fxR9t8rO1vaYd//zi4Ovjp6izPsjfZVhj9Ygysgx+8M3W3JJ98AwJTz6YHnCF9bwHn7v/8w6Ml/xHcXBAIKP5pzdfgl4YrVfGDDVbVWnULGJKOPq3hCxC4LoCFXDJXkFeAUjRcCCeT6w+QDHbbGgxYiuwYXm7asKl1bOq2ojcj0wku6eIVRoiTxWBMpF0F96qwTRgvqBSQuAmzpW2ZmmVYrmUahmfjLP8aV9iQwxzCkEst0bUyfDYpgOdgw/ZcVTcd03MsPCOvlWG1W3AWX5jGLTjCKfhig0E+fasZJn01NfJqO5Bj43/k1YVHB3/El756LgCr+GuD9AvBKn43gARNox8E1nCCgz9padEEHRJ01YAEj35618RXLHANukEhVB1fsiD/0UpsXNzDX0BUVZKg4asYjkpScYJjYZJxTS4pYeBH3SjfMTLPLMFxjTZutE0+7msANhNj1LTjYyyAwiUEYJFDZb/Nl0/lquhx9v3CtaIMES48otawr2ku2JEIQuKikAEJAleJbPVFMiyXRUIhpovqgOcFlWi1SjRbrIUM0/nVHC9u7X/ECiwtIhzNvD2+5Wd7NrPDI6zjNd2hpmhI2tI8reIfyiE5CLWtHAzeKSpD9t0432muhRKAtQmiP8MqQ90k21j1QWtKFGK1Lj0pFx/DtUJf9fXA8IYmGjruKLRRYAaqrqkwZBEj7yspkwoBQ+EySmVDVpBcYPPsZJ4qRvIAfOJPM5xJdNOGPpFw/LVRP/1fq8fO5Ui7NH1ltZcwsHt0pPZdUzc0U1PIRxZc3XBcG39Stq/rjua4LF1XHd2yDfLsaS6sA07lNwfDWNdVzVIIrAMLimnZtJylW6ZqEnyeBbOybZB0VfcMTTVtHgfIvKOplonzLQtIMnSb1m2apqNpJN0DzLrK8Hmmp7oKj8P2DCBbV6qP6h5h4kwVA+NCBtBumwZB4MKK4WgmqcQ2NVW3aYXw23Ntx+URax400GYNdCzXU2kDXctWHcsh5VzAbVmUOEMDsm1LIM7VdNfWPIUSD2JlGaRcsUgTOoBFDquHrcwCk6AjDEdVjWNYm8iHsXaZZPdaxOjzpijdhL50PAvZoWmqujtU1dDyvaERerapu8bXnaL+hfYurGrzZwmVTBJqMUewCUI9VsjkoNkrI3dl5K6M3MIQ/Co/u1/IIN1tNj6JzTnX5Gw2Eu9kcn45w5FMQqWlqC5hG9ZMQXVl/P1xjL+VXXZfpccJbBiPjhXYPiivnuurph6qrj3UzFBXbbdR6alrJaX60QUdFOtz9/0Hkkg3UQR9r6nBd22pOUSjQPUdT3Vsc4TNUMO0faQjVxuGqjNcWaBfTrM8Rze49lF6g3uu26Zd/gGMym6vsE3ENvcUrDSAPMIsuf3gSPF013Nh4jVgfQEzxwXTxzv+KmZM4KvI9zx7FFihabihr4a67WqB76i+6g7tO49orWjhjHwE+YsMRs+1Rp7v2kEAdp+qab5rDgM0DJxhOPSGqvUntrWoRwbsZK30yWhGYXJpXuWUWdldK7trZXdV9s7jH/cHz4MfpoeX7yYHmfv+7x+tV/r508nbzLt6/c795dmpO3hi7T9/e3O59XzfXb/xRq+uPf/x2xvzzdP37vTVrx9ezayf3n680rLnqnv57urxYGV3yRt2MAXppqfamqbrsJDZ/JZdU97CTTvNUA3XVF3P1C3QOLGlJJtqDWhXptvKdPuTm27ayPFDy1Y9HywZPwy9ALkjP9CCISgPQ9NcGTR/DPXt33pPDTfKsm22pQbNhr8m2TpTNdV0dIulO7bmWDp9Ni3TVardIpzkaaZqaLSYZWoG2yzSVNW1Nfbs2J5rUxjVMFXLciQUmuXoDAUg87wNbn2h6bpt6Ya7wa0pAgpXMyzP+efsNnmaO7J0hGDEmqZhaa4Zqo4KLQAzbagPgz+vBeR5wnaTV25Ir2yfle2zsn2abJ/p00fewH59NXq7fh48Vbe+yPvVyvaRDyt6KvnxwApxTEPnTyvWsxZYPg0lpMOKdYCV1bOyev7sG1ahi3QLGYHhI1P3wmGATNNBnh7atodGo5XV879dZfs3t3eoXXHv/yt7ox3on3beTVXxoVzsQQgM0zUcPdAcZOk6GFfWn/a8myacd9PYeTcNn3dbmR8r82NlfjQceTvYH1zt748P3f39Z/tbX+Z9ZX4I5ofGnXnTFp9502pn3rTVmbeVCbEyIcITfPcosAPPCb0An5BxLVezg8CxjUD1PffuJ2SOtD7+/sN9/329M2+hoXojFaTb95A5UgNvNLJG6ki3kRMazkhbXWlYXWlY6Xcr/W51pWF1pWGl3q3Uuz+Geodc5IPSM3QM3VS1oY/cMPBdz9RGwQh56h/nSoNqGbpr+6odhJqp2d4wsD3dHCLdC1zDGqp/ZPVOseYreKZLNDzdK1Q8zWpwSrvqStFbKXorRY8pWOePg3BwGo1fnblXb4Ox9/iHp/FgduDlbw3zxjhfP02t5wMtehEebr2b3ZxvPdof/jgYjy/QR/sq9hNv9MR6PPh4mn04dN6/ePvz+ptX19PBFbwcepH5SN3yno3DgW4/fvnR3f/NeOae/vD+0cA6HP98aCau9vOW/lIbDtyDD/ZHa/zo3TPv2Uv0ZJDsHzz66L6zsmB967mjDeLx9KdD7/z8x2DdfvKLNjg9O//tralu/d1df5dMrwdXzvnLM9d8brrr7uGVN7j5WY/O7J9j82dP/fHq+WDr/bPh2dbVD9MX69nB8NfBr1b65O06Ovv5/Vb6TFUHNx/H4dut7OryGdT3aDC4+fvr/bOt9xNXXfcPPsSD6b5uHurvtz48W99/+vjHwbv9m48fb/afuPvrj1+F+sA+za2PjquP1PWrx6PTQXQ2HHx0Dh49drfOHh/qg7Ofr5+cufsz/4X77Oz5+cA9u3hxaI5N54UXD6yXA+fqw08f3fcvL7KtF0/jbDAdr4/eeubs7c9bT6bXaPDx9aOrQ/NrBqj63H9LBLiy7hHfympWzK37RLdaoOV/uSMjrgcqsGGZru64rurxR0bqWYtUfs10PEOzHMtxLM20zfpZ+QakK5Pgz2sS3CfIlSXWYq3sjiX308NhqDuePnQ0xzRR6Fu6qzmO6SFjZIbIXp1M+SMYAv/uEa5wECXLNMiBd0f3VLAR6eF3z3FVHMkKnk1Ls22XHYR3XcfUVFuIIqWbhmfqGkUCy4htmCySk6XbNj3Tb9uGqus0xBVAw5KEVwEBCQDrukNrMQzVtVwaUkrTYZWgBXXXs1SbVKQ5lufAeiIg8TTDsTUWfwpI0T2PxrCCtUqFkvTZMgAfjVel2a7jOWJQLLZuEmBLNT1X12l0LxfMZJvW7hoavFBqgVDXMlwRiWmrusvIVk3HsF1ToxG0DMNWVXLzAMobLot6ZXiqqTu6ISIB2nTGB9vRLdWh4bQMgHQMyljd8GzTtmhcMugojEdAYmi66bg2KejhuwO2a9G2m6bm0O6GicjybEqtY3g6rHdic3Tb80yTRSizTVApqGwAK0teeTqwyqS8grbrwBNRTjTVtGyLRvOyPAC3aBMs3YJUKjPQTbprOyaN8gUFNNeUeGJqJhMwV1NB2lQaF031oIcdkm7iayFeITO26VmmIlHimTi0A4t7ZpgssBv0tglaBxVC3dI9nd5CMRxLVz2pi4Hvps16AYRXBYmlz54FY4dRAqToOov45kKfgegLSEBGoNNo1DUTt8egQgXiY2gGY7jmea5lE765IOKuh6+d8M0BcShDpoHcGxaVB1c1bZvFlQMBhCHm0oEJpHimZksSCyqRTplW6IWsR3BHUYbbnmV7lPnAE9NzQNlr9lh9lUNolh84rhMaPoxLM0C6b46AOa4/NEAORiP7D+vCsj5rg9Jaua1WbquV22rx/uR93SRfdk/xDp6L1Y7iyn3wddwHK7v+nrts+shDIHqgtiNTR2g4BAXUD0MzGA5HLhp+if3E2on/1u3Bz14SQRpgoXsW4mXvaI3bNbVB5UWhDvaWOXTcoREOVXUUhj4yh/rQI/Ph0tG8eeCFcXV54IXx6HjghSHdeOCFcbF44IXxqXjghYEYBMyL7n0L3Fh02Uni8/z7HDzwwjOQPPDCY4QC5kXb7zzwwh1soYGLHGw88ELDQiBj0RA/3mH6YhKkoCI2aoyFUodHKANi4JIat9YvB/EJzl6rFT2YJOQLecV7P8AJpBq8cmONr8hJEUxeAfo1yk+7QvES6Sj1xxdUC5Y+O3bIXh8zCNyGAlr4dFWBGOeDNtbFX+eNyLeB4dfDakphXwSD1PX1HmjIGI5mAnCJGV7ZB88e3TwD5EXpo+gYKsBlsmSWBugQ5uhWHv4Vz9FryroiFaevAltKbP1RlGZF3aRlUKDKrTToW7EnGpm8SGH+H2wgOjM=</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_5111ac75504b44448a5dd31dbaea5a09\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -273,13 +252,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
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oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_bd219e4e14e14d99998d03b2d380114d\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -291,7 +270,7 @@
     {
      "data": {
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6AUzKlEhwarryL4JPLbA+LhNngJ5lqfZd8Ny6yaW71SDy+00YeEW/E7q9cKcR8qfVFQm6Mj5GJR6L4iAi3/s9Ec2CpvPjEfHuwxT4leDsGF70Rz58gqnboixfJw3veilaFVa1igpfV1f0QJCYoyTUjmtxNuVAbcxOxYknMXswP/n0xrswbRolVkHimfN0sgE+GXKet3SVuz8vt4iiOW29qn3+UOEUfDNxHCCxuX9qd7ja3Z/dGT61viz3xIgTIGWvm1fj+GN6Y8K4oHs3p6D4OEVwfke9kvydXsuY6ga1oV1sOeYM4Vvu+ebtLHPy1++VASk2wB/FKOccb5r8yaN7nde6PmHh+oQp6xoqYnNt86HNSdHEXxb4Ab7S5FzewmC4Wi/jh4RyRKfeyunCx8iZPHzUTB7UWdQoVDQf0CNzeniDN0f0MKdUPi0IwjzLOd0Bxvte9gRlIqwrFDD4lBkNnDpvMUZReJX0EJKK+N/zFfVmlkLlQkzmk4Q5wiVQ6iaU2VMxmsCP5sMrNTaZ+jV+rLoRgnbu8ENL3xLrJ8oqkLDjtzPBUz8TDgKUF4bzlzG/LqeFLg40vkpZw0oBaP8t1m+0F9o//jH5PANfzk72ZAQhM7c8Yof7s6l1u5SMpmQw/6c0poKB2/LD7ajf6iVl76lGlbKIYtkC+6aQ6NyxgcPfTuwcARYbRGnYmVKdMtjS4jvRCEk6Xr9KGGp8uXhqHTbzmObNdDPSzczwLk37xkLaYWimqMvi4jwDLKszmLY2v/yMhefMowf21s3spePvGXWbkZ1XY3bk7kfI1DZVV3Le0J5N79pI6YG72WL7FCdK+D9zRJarqMKriU8zU15nSuvdfGnl7LxLy+rdPFlNPdwtkNO7eVI6U0bvcmX0braMcSZxCc3n0tq8wrni+WBxSZmvd1mhvJsnlM9m1jBrsk7/laPCi416K/yonBK8OQew2+tEN+GMxfxZmLfdNgfu3vbdTrgQej8SptZKky/wrvymU+yzuRObamzenhO5kD4+KrbBTbL1HO23CGb89d9JGcSX2n//NzdT+d6EOSUSenVWkVlTat48imfOo3g5jy7n0Tnz6OufN48+e9jkiWdMnng5ef6tJ8/XP2HyFP9PBsMmGVXCXipOsdoKh/Hv9H6lxAc+oecFOdbUUnZMwqxI2wTTI6NkfMvJ4shYMnI3tUibjQiOOSG3Jjh8WYvnOxjNOuYg/ORERiIRfBABmHgi41k+8GbOCftkqdGMUmmG8H3OPH0NL69+8revkwN5EuOf5FG6BMhnaXX7OjVFJaLbiVI52XCSscz5Vlh8cK7bb/QSMe+fFlmJF4clkkn8Hwbog8IpmqItHRIX+DYfGb5JFfqejqJwaeffVb6pqVwK9XT8ZsJ9rudfS2dpY2O64fMWl8YwglhtsZpXQb0o6Df63TS8yP40LiOekuWy7Zs8/jPGtzl13DXqBJk0XsmCJUX31Db9SR4yjqCQs+YTDxT+XY0PWej1ZLEorS5zxDqpf397EYGRl2QhPOaKyOjpIjJ6kIgstFezMpIsMFdIplv4NCFJFfz7CEmcZy0bH13XcsOc64qcSdDycu4C5SQlJo/nJ/IETbZg8dBlK9gGhzOYuQwY1Acra+lcivWWwJpYl8uuqMiKH49fYE4Vkyt+8QLfBLVI4DlejVxJJEZd2cwFHa9EPgCWpwbeFZmBhTcf5waeAd3ruK0u32x+3KnXZACgF7VBB1Rn4YeJy+lE7bDTG62u1JtuLdzohFz6660aT/Ei9twAk4KVtQcg2NiIE5Bu3EdRkyPADyzIs41tiETIXbBMREnSvluJ2S27I83qX3234a8O3M5qpl5uNf/yLblM/L19F58KTGIa98TDUEnwGbji9LTiIAHnGZgHK7GwyPIP7qQkeIpPIk8yZ04eqSvTi89Zpqscv4dQ17jVv3ybUvzfRQLHohk2uZ6F9sYNnoHvPGon0N09CN3ULoZG49D1wkZyc4daVRLvnczRBzW38PX3JowLASNCamLpvMEfU+vn4k0sQTz51RkfSJz77UQ8LQklMyVviWzbHA4VMbQgkTM5txSXYZn6KKGKVgRreNo2LJZlV1HR4sp1uhPXuMTJD+khsRYfR/n+2+8ZEu3JnKqerzXzOJEcEXHK5lyWNZLiOHsQKq8lMf6y/aV6SiLCCP0T5O2Xb3Uuf0L88sspfZJo7SJKsqbrJEKZQxyPgm5LJR87EeJDHikctswzsHOeJTSClivoSsrHHu3UlJX+Ph5hcndR8uP333wv1e8sUimt2Yl6gJ6rzQ0bBWFtJRd3jl5WEtXhaj+3mk5q/niwxOVJ6ob2dMl/tARPG9ZzRThHONM8eYB0Pktb0CCqh08WiUnxhaMrAfoAEZqC9ibafzZQLAXzYED3x2tDK7y/Z0OmRHcslBuRkEouxCmpzIhycvRPMD9VReRgSEzGEuabAHqRaMc6P2nFXwnc39dm7QAUgZet6C7szrbgf8hHmFRQ9Btut3tY7/aKYLGApduqRpyV6hqGseX0mPCQApNZDuduE1VekNxVfKaIygh+ZiUhQfpDms2vJVlJZHHO1KVwrP76v1q/fJuMke9ffs1s4OEXYEyRNqtScXNGYlTKwiqKqq3IyzRWMl/jrTjTXFIAco/Y7M/uXZrdOAPQ7YXt5NpGPiskO2WRRVxbudRWMnyScvNEPsnCYz6Je1FWMh9ns0kBzGJT/HkmmxRAlk3j19MeFrdXCfKveHaVrpogJjPEPP5KpNO883ngFL5sg77hF5WI6yrCQS9lsE/xAgCK0NRa2JOvJqGPjGzNBnyWXqmevW1ZpZjIYkoN70RcaEb8P9vloHt2eOIGrohCUJexbKznMSU7rp5SeFrp8ihOAzD8RJWrME70T/ymKDbSrYztj6Y8ELcydXadp/oGVcsP8vOkNkVTnKnHPAlNcoUg3YLjtprGU/SP634I6ZFAkVScaeRjiRrnDU+el+Hm1LQieKAamBTOVQSJz3Kkr+CcL2KQr+g5n9TwXnFbo+mPcatyDlQlUYS9cq/XqXswma+uiB5eT3ZtJhBXjfhNWz8vwKcw5s7cGZD5Tn0MFWvzzxG/PGsji0R9njAg//tKQfjz4mRyNVqbjmOMr2maOcCmjbAHMSRGLDnyDhQVb2f8dmUzS0NmIv95NHBlNE0Dz4eVAXpcuG7a/cggenC0bhqTssDGlzj9xDh0jHOWiTkGSIfnEmnW747EvW4wcWQ0HQ8A8Y1TItbP+wvmoS1+pVe9Vdtu1IGY09Sh4MSSTwvcotOYXb98izEpV2VjjFrEXoBRv+auCMUxgITNP2MnyNjEjcN0iTLpJR4FUhQ+QsaBGlOdA5+Mtea7SJzbyYPavBXCzlU8zKBawEy5hDMuH0vf6wnr5JtM8q1F5Gbs/DT1MlCxKoIUa5r4xIPvqxuoaHL7Cxf1sJlJEvEjLdTScqLit1PCMmGCkhZ+cMq/SubZy9mL8mTUKB04ye4qScl5td+IGx/jnifJE/92kRwrpOMCfDvPQn4mqEmfmqr2Xkxu7oihRJPX48rEU+pCjl7UzisFryeF4CFVRvD1xeT07LiU+DApJx5TJeNbJaeLyi+TsvI5Ufh7zv0s85YdEnIgQr8bWoolSXU0b8FhKtwlL8QUEbhxBXxpJYEfHlPxrRn4E6ozzcGk6uyo1aBFyMYrCElsKoiTQCffTPB9z5xK5xMTYOPmi9gy1J3sa5FS22/DNBPy77udqHmmLNP8Y4Sp3QE820ng3Mn0GWo9XLxdHdZbQTQsBuEAPAxRqwBKJeefAcNvu0pt7slWg7VSsir+uKi69P0/7TFAObjud3tNGQPMVDQT6+wbd/xu17k7F26fuKSk05Xn+VezNnSKD8lSmbanEeJsc3jpVPF/Zxqxpv0zcaXGq6kEJz+2pJufAvNRON1+L1qZ0U1izZLLYbKFyU1o/87tyVmDasGCM4wxUWEicVXSwkotoUn6hm7Pv0rIbXbEqE0pYVBPJxpWYtXkxY/419VfVzuhCCiLW0p/+TZD8L4Hba6CMmGRhnLrp+rXZN0qa0wmBOBfCadxfVw8MRvl6YPkbJVpeCJuEf+QFU8HHmbV+n3KiZwdtEg3co5yGyeQawRynTmBXmZzgtcx6Pw2P0v1XNfvRI3GVsom+yYm3bwa+HWGgoJ1zQuv3EGdX+S6wlMsua3eyvdsHUlHWtSz1+pFH+rhcPVbTnHA2Yj8G3jTCl0QognC7+lD79P8bEb9rpAMztNsDC1OfsT3mQLnkhtOeWhLNu7TujZ5uEjptLhkzgnosTsqh6W6rFcEfqNWmMggmfHiZgHOSgY9Pg35dZBJkzjefDRu2UMqFYyeTZ5ci05Z6QvcTbFpVhyInrGGNlVHL2O9LK5CHPhYXAXvjvSCWH7myVTdsxk8J2XO2uYDBCHL6kRwAkwjgLCy37jFFAbxQtkYsxRN+bowRpBf+G0cJciUVu/ziouUYpPhACM9ScdrDWn/+leKZUngQgZYul4JitMOYoZbauvDSirfbQZGeccZz/FhS8fpZsVNz8hOjvOWS2YuBQ+qujCr6lmNTXHk+4yeuhj31NvYOZ/TVRfjrhpDJ/vqbY43n6FNjNv5fRWvF/9gZ138SGdNq5dH9NXFI/pqsji+MvMIyIOmr0haBNnZ64FTzIMmmIWEiDjdojn0ND5q8MSZNC4/PZ1On4rhAE+fHL9OZkVZ6WY2o+rXwXQG2PlU5Gf2GAdA0zPPyjb/EAYveLA8I40L8iFnGzOV7iTvz8MmKy19Y1b+kaL0iapFPJlxEGuqWPZegPwMIQ+S1eFVGDbyZDXWj26jB/VOB7aCsNFzL7iLIcuuFeWbCVW8tEovXwmrLghONiQ6uYt6ymhf43cvTL5L3Jl7b+Za+kmeTAPyfe0ZcpVrALMGhzsEuhsPdNnH+emSBVOINrMHoacpeq2RRa3beKWRrPylan05g1yezWYqnU2a3NRjIX2CMe+o38x2vNQ2FjaksKghr2c1pN56VEM2FjckZzEzieLhPvD0j6ctvUw7ld+ykVwxMH1R9iIZ9+MhXK2UkeF/Z3TeaoZDKehUHFcGmxOVfcou9/zM2vJ82Ul/5aSrbbvdbn0QvpAXuHxPrYnl7aKY14G5AYyphLUyUHXcCivq2p6fl642nV0omWg0Z/OrDF4ooPhNDtw4XJwElS830+mZplPoPTyJ3lQavXQSvOm0eVPfJQm97G6x2YnxpvMiiQsl5L0NavLnuSZASviWiBeJi5ZActSsmzkZOz5Y9IRdpDOP8GDfba/Mgpqc3pkL1hQRR9nnK6hIzbA5EzZhsdZbPHvHRtpznlpXyD9qkg/MjeNqQxwGXhlIkZ4JO/+0UQ7HZp6jyELyVo098xW9aK48KL67KHw9q839Hq9QdFL7LnNk49m8s2HjQ16pQTBXPpIgs4QjCTNjA/KzzKLzOZ8WptKY8y87jSlf/IESP6vVMdqkcaReZduuJ/cuT4HlHn7ReFCeDwFwNcfLmVjs84KZKG5snGnge9j8dSb+uZu3p6Dj4MqGOY/miReL8Ty41PZ3P2wlE+tMV52Sd5wHmHaXshPC96w8yBng7yMSsr3/MalQsZ7/uEjEE39aKmZtWRr35XQOf3XG95u6LCBjE72YfiXtuJx0+i/yXnLo75vPvq8JK6x3VRcmwGkU9d5FQbi6VryKuj3wNqutbjEOQMUZGeHn5ssSWM71du/1y1KvE4ZdH6aAjU6/tXEVdsLXL/kGd03ssHr1vBo1An6bx9cWYH7++qXg0+uXYmFJ45bDq+f+VejfQBue55b52otqtQYvWhKF0uhFto+vrueBF/z89b8avc3k55VW1BMfV15fu3dFwQQNiAeIFBruo7cmgDGISh6/yre56mszEGv/53//X6hoEu3/+39R0dC1L//nf/8/qIiQaa/Dp/8bFW12qbWi1n3YiV5YZEHt8ef4L8WuZJMFdHgHb4IweJ7+xg+Mg0QGX30uYSAlc7/DR74NVH2Le/frWC4zhTPXbDx/fRpP9VIyisViTHeuVAiRUX0O46BR98WYKEV+L+xtdKGM23z+enx3lhxXQjjlE5fNzXFspCruC5gru5sSrAg0nEVRK75fQ6HotVX0gavd1K0iqyu9sNnmdgvHE3Y6wDCwEPnJk4ktK69H2D87flcUnvQqR1hUB8Sz+GTbV9ZSSgOw8SLjC0a09IAr5l2OIcmd3KzBlcaC0Ri3Je7GVJOez+mX624E8vPtOd8c+fyF9rwi42/jDDvgPvCoITjMmit2AqwVtbfcgi3J93xPl7wWQUQcn69rzxOXIXCM5u3uJ8e43jq+NmvRx1qhXCkZjr91e1qhFj6vlZzBacUxy/dvK+RNZByValv3Q+eoHI2ujaNmDZX23tGCc17DbypsGH16X/rQ8u8c3A6Pz+2hebtXeOdsnTpo6O5XGGpHFwV9395xume10+vS3j7ullpv908deuq17i2yi8slXPl87wy3D4fXvW73rFsK6h8Mx9zesa/xzccLUjg4NqlDzltn56U9GviF6NBvOSfbN++vrXJ0esKO2++Gjnm2594Xop3to9J2s/zGQZXzT/eFIQ1OCjvHFnVuSfvkvHBRpsOCfhMNndHV/e55qbw3ZDZtbhEHWnV+bu+cGSel48rdvWNc4Ytzs1x4s1faPjxpO52D6sf70kmp1y20GuaxM9p6e1Ap7JC9WuF+d++TM3z1asxqcV8F57RSZfxD5m4I/rV8srP18/+TdCSSW0BV3567GP7CnEAEPwwdvMXnk9PN8OrLt+f8YAenC2Dhq9CB4hGgNMwL8dfiWAe8Bn/0OSgiiewSHkazsKFpbOiFNgOZxJU5UMYR8tfpJFOiXTGnJ+moOF4wleDlBp8FCEY61U2b6ZZl8U/uHa8w54sKdMNX6WE/H6cFFwX43GJYNkM6ocSmJp9pno/Thc/AGd+czVvwxbbkgQ1DXOmLDSIfCRaPFoUvFv9PPDL4SfnlxPLRZgCM+C2K8uZjxJ8NIAFjedEx5i8ov6rTlC90eKEjA17Y8kphwo+O8Bp0Q0IgnR8eEf+TlVi8uM1vdkRIvMD8iAlF4i1/QU1OMq+JiRIG/6kb42eOzCZjcF4jZwO2xKXFBmAjHCPGl5dcAFOHK4BFVI6TWWsUUq7iHlf3Mj6Pywjh4qPvOY+hzQX7kgYR+V7E4AWBm4js5KY1Lpfxi1hMF9UBvxdUgqcqMTK1GIuruVzc2v/V0mDKTMPJj98vvydnMTVjwU+wT6ZsoikDKmMFzrOW/keriA1eL7Rt572GFLJ/1Xqb+bVIAriVJPwCmD1l+OcFN+nC51o94OZq5+t4Ug0JDICQGZaOCDHC0PMM27NtFLLQ8mzqKvJ+IyNZE2Bh8BBjOeeTHzW52/l1nokpvgF4w213+Udhc+f0SQbHf+Xa3X9a+3wuR2ZL029szgsGrn75goqM6AYmWBOXRzDdoMziV+UWdZ1iytR7HcEMYRnit40ZzAN0sh4ADr+uI2xqApZaJiGmJcuZukkQEfhsE7SyZYj3SLcNjIiVxIFsRjEyCf9umkCSoVuybkIIxVi8twGzjhQ+m9iIaUkclm0A2bqkQyeIw/DfBpBsEUOUYzBRUEwEbovPfZaeogOebWZRJtsK7bJUu6jJbCTbxUwLUZMKHAxwmyYykzgMDFRbpoTFOrOwrUmaQZpMQ5SL52ZBB3CG8noSOGzgI1awwH+DImRMLkGGjtMpzOsmlcyQs7gklDGLMVkhqBhqqAYg4AexrFQlwEsKqIkkyAbGUMFo27Bp3ADgATJ1SgWhQDHV7RQOU4d+0CWjGSUGoBCwQD2jpqTDgBaaSAoWNYlJdJiBkwyzqI6JrYs6gH6gWwoCA3lkRNJvEKBfdq5NkQ5kpOhg0AAbpE0ymujUkJ0MTMcGlR3H5RQaJuiDaixoWQoHtkwQdGbITqY29Jbiow0iQWQbkYUN6F9Bq0kNwzTSPLXgHZVtMW0MFhA2LsGWEBe0vVKaaG3GsP+xKcULdN2npMqozgjQ64bUR5blMVLVfcu0f9sp5T8YdwErZL5WR0Kpo1inK4WOLjWhzA19GWxZBluWwZbYyf/z/PktAxj5QQkei8BzQxH5wYNHhSJ+XkBBKLtxBAE9IGYwFSJAy6DAXycosPTXn2pcmVWrqgeYUBL6YKqatmmYVkixbmOfOyVLf/3Pa9f9oZ10VOSuxA/992zsnTwVBeg9uUT923sxhlelyPdp6GNGQs/0kGfY8JWFpue7xP3LejF8llwQnLKEvFNbCPwGLhpC4nGRTCJTbOnLLH2ZpS+j7P39evfaIW+uonta/Xy7V2ochHcOal5VK+yEllmpFg6vHbaLbin7EV/iAQuY5AnrlyTfZyB/1NVLUEiM2rplUmbqmOrMHDsfuZ8W+CIGtk0b/AAdYRtjmweEU75JLs6lq7Jcv3z4+iVJV0KW3tDDjDTs6Z5RRZZOLUZsMNNsN8SGF5IqBu+I0aU39Ge3D//YC5diMYoalHJ1npgq4AEXdYtRi/Blo/EUYWmJxTOxYmYxuTLGl2ksHYoz+WARZOiUr/tw/hlizU67/B39H+xaRtU0A+KygFCme8wzwhBcIL9KdN33/7L+D1m8Oo+oDhKOlce/oReZEHC9aE4EHOtLF2jpAi1doNhNuT7W3zukYYSHhTtj76hwXOruOTZCe9elj/t3t6XukVN17hstRkl7d/9jibKtlmNEndZ54bZKhqXbu0Pb+UQ+b6OKG3ZIwTxyDwelaPfOK923wp1C4bRWHdydWReeBX4VPI8qwQDXjq1Dut1nR4UDb98cnHas63PjwL7olkZOq+Tcdncuzs0hDaLSaeEEDw+7+MorlJzGXqF5XH433D/oHFF7qO+dleyQtZ3hu48316zV2jso7Y+umWN6/l2FdAtvy4UgaL0Z2DdvvXvmn9F66f7tUdnpjD7594Xt7dN6afhRv3D6nvuOFg5u2U1p//NVbXC/v/+Jlq4+vj0qHe4dOANq7/cqrPnm7qDUjvS2g6uDBmXu4bZVwP2Pd851eTvoEL0UHZWaO+jMKb85KHtk1I9Iod8/3HdKgdW4prUPd3b5rnR4M6C7Jrk3z2qWXmhsDSJndHBMzplz3bot3bl7bwbM9sm5Xf1IDsAMJOcDsl/fOWT9ci0qHR45I6dwa+/RQn8neF/4tE3eDLdO68GheYNujwqEjvYHbPt93bNLlYt64e7sxBoMP9mIoveHbVQquzfewBxVPlYK4U6jX7r+ePzJuR7edSrMKA8HZdq73nY6J1sfqNmk/rAQlC8+OrXG3ta1GXpX5+Xq6ZXvDPv99x7yojay906d0Png1/fv2QdEm4UPe0HdOflwWDi39uo3B4Xo4ODCMZzPx/eF5oe9cmm/sPvZMXZPDu6BBZ9IyS0MB469feAelo5PKzc2sbwr5yoc3FXMejTaKeE3wXDQe4tqHfQH2Y/8m/z359jjPDfi8JOiBDrfdIZMahPDIATjyR7nvC8L9zgzW7ctQ7cQwTq41XomSJCHcxkj+IvECB7tvi/3OP+ua6bgrwAhlFQDj9gGcqs4CF1cNYkOo8YL//OuzF8nOvC7e09/8AABLmLLILbNwwAbYsuoSQ2u6/keTZOaxOBqkXv/homYYSrv3zAsbOPEjk4oayEd1K0hgwuUMGzGWAk1AactgwXYIhalVCKykakz/jA5H435pmrGbEMGJpBNoYAKQYC6NiniW4phwsLUAOrUF4PCdIFRYr/sBi/LtzHrsl4gQSfEluCEIMtkfNrhhOt8vwKTtOo6IoZFkhTxBmDKqRKIqE5Ni2+rFU0zDR0mTEMSwXTdxjy+Dl+QhWz4V08hQnzzNbFs1TRogNi3y6uwbdvURWgGiMA2BaRUshKaRXFyG29sGzBNNg0RxLf6ZoI3AGBgyic8gIdZkhmGIpoizAyLqsIGZaaJVcdaBsPZ/uC7zIE8BQ58tpGKGkGn2iAjqgsYsNckMtIEHWWAOGip/jBBiDA2VNcb0CpDRZCIYVNb1+NeBizQuxLMxKZBUh3LdwwbzI5p1m1KsbANeNMw9KphS/JAfk0ig1R81zUFikySQsSgW7HYry73QJsUFLAUUx3zTe0SEUHMRAKMVw4SqFvUTPGI96rY4yzKMuALVuAwJpCN+NZ8ziMQNNPiZwS4qBGkE53qKUQgxdiOWRqbbYmO5Rh1y0SGEnyQShggRtxgqhvYivkCHSVGHQwWwmxqMDPdH3wTvGlr8ZAgMBTVKDAMGIG6qQusDINNKLFCGRgtpqalh74NRBiI24OcP7pp68L6EuDIInJsQdswI2A5xmykzAJpSHesRRBwQAUcgX5bHCDgRNh8wOqyrAXqBFuqnUAaNlNb1EHeYeyBMau6QDegnSY3OGUM1LZg2EqKgD4aayYEg8XUM03j62Y6tamULp1LmilFDYalyTfTryudZVuMKvIQQyB3JDX0GQFxZKoBMOIoaL7siLVNi+E4IkuAkboQXq7rdESxHg80ZFpiGHH6gASLEiOtrxBIFlM6FhMQF2bL1mMQL+hMKrACK2DcqPHITNA5zLJSrUcwXm0sFBGfGC0Y8IZUURgxYojDCGKHv85AqUuxNiyQPGhgWvEB/xFhUjlANTZCRGpfDKTBXMFEo4EgYulKSfMTAoDWSo8PxI8ejGnGCBSuaqdFddtGkjxQpvyUi9JqMMwR515KZmE6s5gSNQTMhoFjxJMDDHDVaNBq4DQYhpr3wG2xjYyEwKCmwr0RWt8AF8rEv9NZBoMEMAeCSgmhM3TT8PgotvjREbAbQ7pcYPqJJuRNOOK1VzsjbretzjIi/wJ24epaLLzpNq9pPKQK8gi+7YtnXzRbZzB0bZiLQFnBYKIw3Vz+JnLuc0tGN3xmBFWCEfFQ6NksDLCHSTVE+S7S9HLMuNt5vh3Zwn69xUdnHssfvSTlw6BzkUf4KPT1qutimwVgCnhBSEnwdz5YBLOV2IKqE+lUidON8T5Uy5o4VstDRstVqeWq1Dhybx/RkXMw2Ls5HJ27dzXb2LkYOgfUuLrGzQ4L2db9yZlzevLZrdwVTrcu7P3otON8vvl8fziwq06N1T8NImc3+GRUhp/w7QVzPmzbg3fBze75oOncD21sdulx/c3ph+sRLoUjtt/qHQycsjm673vGVY1VT9HHwZbeOD0fnu2PLlhkH1w4lbLx9tyouYU9Rm+7x4OtsyvsDZeHjBZnLUHgbiFusYNCNHRuIydOHeV8WxjVB6fA5KeGwUBFBGEeocieS8pBuwzsL88p/c1j7lWsoyp3k/3AJmYATl3oM9vyme7bvuEuHae/iJn4hw6/yyQflmGruBEocIsaTIYKkUF1jG0VegEtC3qcyQeh0hPRd8EnYlBTBTCQYVo2waYqSrBlqQ2AyLAsZhFVA4922oim8CALdLQVQyODTyzyQWc8YYOuHixmUxw/AFozEacR9BgY2SqcDcNMp7ZJ41CLzeO36oucwRSlBrItrKfx8AioqYIrPPjGiKWKWnzHe4zH5oTqEgzZoNvT+SagKLIM3Y65CUwZ84cHB3HMOWYwnSr+6BT6xjDS/AEvl9oydAVcQJRS9jvlrQht3wpDQl3DIF7ImIVMcISNIDA9iwbu39e9tG2xYGeGG4ipQ472OB2RvXQul87l0rnMcS7bb7dsxzodVs8LN/5bVJLPHzLPie+f8uB/j2e2dC4fkBLTRvyPZcPcxrfzJ3NiTn9a4FqqEjCFG3x1SZ9OiTmNculWLt3Kv7lbyXCVgtWMPIIsYjPiYYy8wAyMapVSxKylW/lXMA//4IkwoL3ij8jr96DfQpXTRAKM/wgK9ftvguL3cRpDl2Dw2m1CDUpcYrtVM3BpaIS24TPwg/+uTiNOJcXBKikO5klxlt7i0ltceos5+Q63y86wXK5VWLm8Vy79gZ+X3uLiFCQokXNkYcJDPJXwEC8THi49vqXHF3xF1Me27lpBQEJCmMdCQzc83aPYqvJlxaXH96e27f7Qrh4ugrb5sf+ejTdWPxXF75jzgxm+ZZIgwBazCV+wDxFxXYaIaQQhMo1l5vZl5valJ7P0ZJaZ25eZ25eZ25eOzNKReYRxZRO/GhiuH4IwEgrSg4nOiOkaCFHb9/HSkVlmbl9mbv8pmQuR5Vd1xlzbJjDAPJ/YIfG8aoBDz/9rHxPTzPnyTpgQeN2OJZ5v0sw679hCS39m6c8s/Rll89/s+oFzVa8dX7PhuV+zd3fetpz+tt07N8jIuClcdcx9B9ePgkrpfX90U9oqewdOrdYM761hy43s6htz17m/6t5W6MXR+Unh7Piu7QzhoWLXyRYq2Xu1wNGt3Xf3rPzJ2GNXOxdbjlmpnVRIxPBJSX+HPYdt31r3Zm3r/Z699y5840Tl7a179t7s+oXSPsVOq9Y+rNg3Nwd+wXrzATtX1zefzgkqfWaF91H7zhnSm3fXjOwTVmCVoe2MTvT6tXXSIic2OhjuO6WLPe+6NNxpHxW6295H56PZeXNeCK9PLkqdPYSc0X0tOC91h4M9qG/LcUafT8vXpYsGQwV3+7bltMs6qegXpdu9Qvnt7oHzvjy6vx+V37ByYfc40B3rqmfeU6ZXUWG4W71y6teec0+3t3ZZ6Xq3ojvXJ3dvrlm57x6xvev9G4ddN48qpEbokd1yzHcOHd4e3rOLd81u6ehtq+u0a4XquU365yelN+270Lk/3RpWrOGbT7VCbfe94+ChcX1Obq7KO6Wuc9t2OvV3x9dW//QUlba2d3xndH0I/DjZbpyUzpym7vSuWu1remJW9gpHA1xyhmcV596+2G3ulT4fWF2nU/Evrku10R0r4OvdE4dUyPk59s+2WenN8YA4N9t9XLH069pRqX60e+pEJjsE+uq1ciFs7TUce7vuV2hUEde+N7YcctfYuS4Mj66GhTeV2rWDKgZPfWi+vSkd39ogD+0rp8Jq+9Ge7R6za6d1ttU4N2/d826pfth979xvl61rXKjcHZVccDad2+vb83vS3L5/X/LbNx+B/oZ7z07a9xeFQvnNrTOI3Mo5O9oyolLovK86nau7O+DvaWVYGL41tp29Mjqv4OjT9rDQujkpO3bZ2zon7v7ZRclsDj8599cfWIWe1EK/8Ll5d+OY5f71tcECLyqMju4Dp+HtXFRYWGpGJd1xr532wcH7+wI50i8KO7tvkfN+u7FzXtgZdKLCye6J6Zy8+iunWfzJ186bT8jIaObHC8yn5GNcEHz4iVtsbdvkmaaYZSJxZiSxxXbq06JIBCbUNrBJTUpNTCzCcjbZTiFdRir+vpGKp6RlNNO1mMtwyAO9NJOE2Paqnst0wnTkEcKYb/m6J46MkmVSxj+zY/hHv2+e32luEoPIO9pthEz5G9mU8fCcuG7bxJbFDHE3OmKMEoys9N3nxLDjO8IpY8gyiLpg3dQtdW+5ZRlI1+WF8wANUxHX/ikkAKzrVNZiGIiZTN70jnWYHWRBndkmskRFmJo2hXkkhYSnELOwuhYeSNFtW14tj+WV5/K3KdMccuTYYtSmWgqJmi8FsImIzfj16LxGxgzTkrUzA8ODpBYIZabB0kiIhXSmyEaEGhYj4i53nae0REjeR67zQ7jqTnYb8RR96UvlgQ5+jZK6lV43EZW33BsASQ3JWN2wLWKZ4jJ3mA0tjieFhN+OTnm+Nd4jPGWYxeTN71gkPxTN4admbUtSSw2eVQ+nm6NbPIYkuw86AEwJKRvAyjGvbB1YRSSvoO068CQtJxgR0zJtJu9i58dtZRN4Okpb3StPkUjxJu+Nt6AAZiTDE4KJEjCGEUibvGTeRDb0MJUXxMOoJXYsMxbPFqllKLGBt0g0gfckAbtH9hQIsshaxw/dmrpMTcizfpo6sjNdDHznuRhljfAdMfXbNmHsKEqAFF2XAokZ9BmIfgoJyAh0miFqJ7w9hhQqix9sNhTDsW0z0xJ8YyDizLZpujk8VahpWHLsUJBMKQ8MEcuyJBIQQBhiTA5MIMUm2MpILJhCumSazUTCPlP1CO8oyXDL5ie+NcUTYlOSVQVgvcF70SPQqXG3An0M66rrAaGum1KSeZZERNMSixnYhCC1crRCZxOlQ6ALYFCrkUuAx0oMdKZTqDUt9iY1KLJk98HQwRhhqTcIT5TJ1idmr0RIGAKRTHcxjHieFZRIUqluMcUHZmFqy5GLTYtnL7SkQIrM52lKQCz5INZVL1g8iaXsblBLTFJIoBob21It8B62jIx6RIQZNjOkKqAWw5IPGMYONF8iJxbwQVNqgYDuQywtsdBYoppDTeEQyAFIeddrqjk614+CV5YhBnpm7MCAQar7bNC2BDHZdmsiJyDo0Dii1IUBAqGnKYEZlHNbai2ePVeJPTV0UHKy62EU2UxNHxgTk4+IFBKgjZimnHcwP79vSA0PvgMCRSenD8RTMsqhAXoa+JPpYptz1pJ8wDB0qHBneHfDB0tSZfLslLrsbmgyQGsZnpgW1w+idwye7hFhNeQNmBosQ+oTkFc1N/FZxSQ0ox5hkgWnxpRjBJQlVT0CsxiDcS+7GOZoQyLhNyyhrGYD4TZ4cgellHA8pQJ3QMlrqke4lqOiIsMimKcCTSPhjVejlYGCB4VmSr0BakMdL+EQPAWkHIAwqxk4rZR0MGeRJZkp2GeqbrWAlngig6mEZ7VcV6kxQZ9av+flTq4P/Ob5YV2e1AH0iA96wqiCxCBUNT3zL7tEYv7QeqC5XBZZLossl0UWb/N6amj3526ZekQEdLlhahmG/G3CkMv44NNMFMu2XE/3ECU+mOJelbmB6fmWb5iuEYLt9OiMxF9yN6Wkja2fkaXY86rEDTHPpUWJ6VNwe6qMhlXKr5xxXf8vvf1k7s2wPBk9EQaWbcd3v1B19wtZ3py5tLOWdlaeneWNCuXBJ3bSpwX//s1Z6TOqnA1OTutvKgVP/1wudA5qn4ZbhcqHjl0/3euWqp9rfafk7Zx7ZlC9Py6Vo6O+0zgmzfPB2YeIlHr79eNBYbBTO2SHo/6tTcm7t461d9CoFHqVe6tw6tT1Abtxo06pf9L3C/b7uzdOv//+4pD1P5+8LRuG/94xQ+PwvPC5YJLC523z06BjGaPzQn3f6pbcu1Pm0KMb597uG6eshHHvcHD6oVG7ZsG7rbvyke03BndvzME1dm9rO6V39f6BQ++HvYq97X1ChZB97vDtD9a1cXKx3SzdfiBNJ8Kf33qlwfvacam3e1wfDMv943v2/sg6KNn3x91B5PhH1Nz/eHpbKnVZYxAdU0RL3lEUFvY+vjt39J1DSs3bXfamdF0/vRvcDz7u7FSuUPm29GnnU8O5Mbv3FVq2h1Gh+7655zQ/2df3o3eFg6PS2/Nbf2Da9atOqdAZdUuYXViDxn7drpgm8L5UMg92B4W3e5Ta7XfNj6UmqocDWj7zO6j8rs5Kwen22eDMMbqdUuCyNwW9jyqOtb/bhPLW3rBEnJOqM9pztzusVD9olkr7+Gpw+BmfX+NPzbNWmQDk4LraBR5fvY+OC92WSZz7G9vq4I+hOyyxvt0fDKu120O7Y511yv1+7WwwvC/tnNtNqzCyy43D6gBH29de4cR60y8Ee/Vj4Edt1CmUGv7HwtVZc39gDQb1Dut3o2Zp//xjxel/JvohOugd66XRLisNGm/Od6/J8Y09KtyfDMoDyzwZeNi8fXtR2hm8xc7QNG9/24Phf4KbM8kTtmnMveryD7lNQy9aFBEb/qdjtf41vjhz+svCFNvEsjHY+RYGx8PG9tS9mdMol77RcovGY7ZoPPp6zqULJi+F0V3b8lxahR/E013XsHRm6NUQ5i34O1hu0fgzO09/7C0a4jIH3bQR1238AkMLE4JsdbubDa612GQgronTLQvbmaveTL7sYsY39THdBjB51Zth2WIhS2IyqG0wPbnmyFOvMIZ0QuLLzXSEyfg2OGTzteX4mkdQwqnlZLGYhmCeijOFq+lNXr6G+dqRupER84VcM7XdgGeNQXyBUN2oiPmmRQPLDNx8v4FpqYsXsWmZFDBl7i1kpiVXnjjDdJuYWFUGHCJEj2/C5FshdDPDMGwblqnbJL7LkW/KkAyDmZchpG4mNfmqGMGZwhblC72K2wbmWyljhmGEgcfj+yJhhk93FeYLzFiu+YkVeGIxS+U1p8Bq06AyP7oFPUL1bJu5bUCYyC4u7j+kfLuCvJGRIIuh+MZAQhkmWSFBtoF1i6gLWBHfA6puYUSUb/ug6nZKbJi2ZWUvE+R3mlJ1v53NbwCh6tY86DcobMlk6SCblmFMcRtaQ6Vsi7V+oE9RavLtAkSP+42Jpcd0YWqADYH4zg0BQvlqa3ytJF+QtNX9idS0iMlommEW0m2dqIEExomtW3x9EjBZ0K+mrb4AW5BppwqLBU8TKjekkEDnMGaoW05NxEydqlFlEzCPQLyThXW+BQhRM77kk+g630ahLgQE6SbqmkHDBpKM7NWN0AX8DkDZVXzA85tFVX55alKgRF4TyTDPhZ8ZVbqOTD2+CpWvQTMS31zI+UjUpZGY6VA5JumagTYuJfKqTgNTKrd5CUyGSZhCyyhfCWYZ8WQ8v+94FBr8YoH4LgL4ybe3KB3D9yTRrGxTzBfjZT8bNsNWzGCDwh8LyUT94gJXhqfuuqQwAHSleRBicjeJHEjMQLq8Rdc0QJ9B89IMM/hYQyS+NQGqQ6a6CMBEhDNTaAaLQAvSyoCLDignYqr7Gg3K967Z6kJWEEhmU4nJBtaDFZHVYaBqQbPZ6opO+KkribRBsglTN50aoK4MK83tsasQ339qMMuwYrUFYk/jgYG5/WJk2kwJqGqkBh5oLejmWIcZiE8TRClxg990nJEwUIvwQc1IBuNXSeD40lMcX5TKJxaTGPaU0gcBNlF8264JnSYu8hRzG6h8cSMolzBoBJ8BskofZJ+pOy0w7zUrHp8GYjBbETU+kWGhbFchm+9hVBdQ8L03SFygym8eBlcL2/FVqVxPADd+zy0KlBm2yzcv+qhKAug7j5muC4PPoEHIPPqXDaOTBXt1hYClL1C3lR2IJ3agvjzCuYyhL2PocZzRwheNgfEODWmJ3oyOSp3zyBwed+4iWhhdsYNC98PWhWPv3u/ck/07fFZo7JbeDGrv+0GnUHlTCUsfz9HuSbluXXm0vn21XdpDhQvH6O2cdFgtGhQKu+/8iwFt1D52fmoM9E9wNGxu/PKnRRyZTXSwZsTmQUvcUDQOOeZ8WhBzBBODX1EONjs4hgbfypcJOeagXMYc/yIxx0cHA5eHwn6/iKMJ5rFuu65XxR5BOnWJabqEe1aWa9IgWObI+dPbm3/4sCOPmYA/psIa/AAQ1pXjbIDzanHVOZ6RDOXZM8SjhBnvCpxGfkugDFOA/w0evG4qP03OQCqmSKmNTKx8R/BDqW2mMdk8iBTfYggOpY4p3zwOUxX4mUyeTBFBG4wZUc429yMtK3VkjbuvDNpjKkeV8hNcVBUGX14fB5JM8M75ASEZDTTBAWbkx92/HzbWQb2CCb4XcIP8y8S+rHphQFnV1BFxwYlntlXlV/ZVXcbjhzgUVtT4xgYCU2zIDAugiRGGnmfYnm2jkIWWZ1M3BewFuu5TUmVUZ4Qy4obUR5blMVLVfcu0U8Bm1arqAfjgJPTBujBt0zCtkGLdxj7UZqaADa9Kke/T0IcuCz3TQ0AHfGWh6fkuSZOBPd0zqmCSUDBHbAC33RAbXkiqGGpiNA3sWkbVNAPisgBo1j3mQTMBuV8l0Bo/TTO8gQEKTQw8kBzkVnHAN6qZwCVEvTTrDBIQAxNoFDGIbhoeIfwoBMACZEjTZPgGpkQ3fGYEVYIR8RCQzcIAe5hUQ5TGjH3A4CKPcJS+XnVdMJQCGHteEFJw/1PAC++WTfXgolsdk8ALrxdKCdKiqz+SwAvTWKfIWJSHNwm8MK9cWjYW5MZKAS86op0EXnikJAm8cHNnqgcXbahMSd2iNcsk8MIYU2qkLLJNLjdVxCDyO1HUy40ZxG4914QKSIFnHPnnxbGy/Mo/P58qut0AG4pXop6LPn8hquFeD/f54y+dEKwuP/xY712tpoqPkVY7bq0p4yBAT5//LPqgeXthRT3uKgjehhi6CPoaLNxtPoeOEfPv4I+vNsKeVgd8aBP+ejlR3cVG2Kr1ruBtobCmfdM4nPwIwGPM8LjTCPnPrdEeII9Lf6lfQgW8TDfqd/ywAsblTB7+Fzcun2sFLVNcPqbYMsZWrNY73bhu0TIoMPk6iaF8T/dELpMXhUz+f6fKVUQ=</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_de0dc3b56fa2485189022ef2653fa1dc\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_bd219e4e14e14d99998d03b2d380114d\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -340,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -415,7 +394,7 @@
     {
      "data": {
       "text/html": [
-       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_3833efac2b3f4ec189778c92308b62e0\" style=\"display:block\"></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_3833efac2b3f4ec189778c92308b62e0\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" 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+       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_21eca24c3bc14e6c8b5b9ad2200499bd\" style=\"display:block\"></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_21eca24c3bc14e6c8b5b9ad2200499bd\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" 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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_21eca24c3bc14e6c8b5b9ad2200499bd\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -427,7 +406,7 @@
     {
      "data": {
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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_3833efac2b3f4ec189778c92308b62e0\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" 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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_21eca24c3bc14e6c8b5b9ad2200499bd\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -480,13 +459,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_c0fe091103b746faafd7ffe0d702ed8a\" style=\"display:block\"></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_c0fe091103b746faafd7ffe0d702ed8a\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" 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+       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_28c54a9a4cf8476087ce1c9723f4630a\" style=\"display:block\"></treescope-container> <treescope-run-here><script 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oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" 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<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_28c54a9a4cf8476087ce1c9723f4630a\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -498,7 +477,7 @@
     {
      "data": {
       "text/html": [
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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_c0fe091103b746faafd7ffe0d702ed8a\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -547,7 +526,7 @@
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type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_53ffe2c430074373aa7a7c266f524ab6\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); 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oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_32bf0ff96e2e4ea099a433e87574fd1e\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -559,7 +538,7 @@
     {
      "data": {
       "text/html": [
-       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_1330c51f61c44e1a9e2a0526145d6a75\" style=\"display:block\"></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_1330c51f61c44e1a9e2a0526145d6a75\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >eNrtWQlT28gS/isTp95iP7CQD/niqCcbXyRAwCQQdre8I2kkD8gjIY0tzBb//fVI8i2O7JJkN7tQFZuZnr6mu+frzq7PJzbZl7hHiK87Lul7jsPR78h1fMqpw2rIIzbmdEx2kOkwnjXxkNqTGho6zPFdrMN6MKCcZMM/asj1YMWmPs+GrLN84sIqcxgsa1i/sTxnxIys7tiOV4uO7qD4L80GAuBHDT6oIZNyIGOcML6DhpRl4/WcLP8HeDl3WZ/eU2bBOccziJeFpR3kYsOAxaxNTF5DeX0gtGEkOyDUGsBKTlKEPMYxBeNm/OMv2TH1qUZtysFEPOLOjDZLGfco86kuxJJoN7brYXc78uPuzI9Zb8RApgdrvu5RlyPhiL0N7Lo21bFw7bajcyLc5BE83NhPpzN7++B5kOdzZBCT+WgP8QH1JYvwM7iWY8cg6Yw0cHwuhftgGuGo7xImTFZ1wVUc+vnXpJ0OZoZNYJuNbHsnkiCBmj3HYbCaDhzvJoMWdXAuYElsLS1zqotFl3im4w0x04nEnCCdCQMBBKTXdlA2OrSLCvkM8KEmSq9oLdmEWXyA9vaQLEieVN0jfOQx8Dsitk/mig1GTGi2ytofUJML/UIC8eUBfh+RkIbwY4YTSB65HRGfq4wOw+tqeXhI0pFPMoLHzpogd+QPIjfuJNg4FbEXmfGElS/XQWgRXSR3LMuO0rcfphhEqyt4iRVi8y1ExhDg8U0K7cK/pRsyEU5PeSmhUEws6Tb2/feQxTHfdGrGsz+EMExNhT9kwJ8Q/mGM7+9uJyWAQccoZLiXWq4zKcSxBpaSu72UnILU9fg6icNARXAGg62nkiHZA2lxZmp7CpIxqndhweljTfPIOIyfsP68LVXyWJbBqphAd4ZDOLhAgcMfYfwKCa4xh6drA2dMvEwCfUzuiyJiLTIs5JScIgjACuJ5xOi7UAbJwLGhpi0SlsVvXIVDM2qIcgzVRBw2gRxrYD4DvzxSv0HnJbJ+5DGgNqgPQifTOr1KiPaRjTVi12oagcwmC1rp4c9OoryoBGdzogbHtRt8O5NFWViYNdsRRf9RmaFH1yUb2LvxCbYgWtj66fAyVpYG2E/vhzz3E/0QXaA+IPoNMTIZ9N/MXAdxNPnQlH5Jw/AtqaGNX/KKpm98T/WWDz2qZOkbKCnuUQgeeb64QNeBF5V4CXKp/3piw1QIBWXDGuA/FuOvI3VuHid3fF2KRP2+ST2f9x3WF+GfkFpPpZKUV0Q2JV4V+tPqRze+qqKwaog9CzBQpEaY0A9/UhrUQ3eijTgH8JFUgObbSUGbQqkVKnAkINBk4l9IrmikVgDsxhGGqKDYRr3JUHNsH52MuLDXQI3oJHy6E0iMbEC0GwCjUeUdwvsyCGEnZhyOU+wTYwZh3xJZ/O6sh3l0OoSOslQlw1Uro/xIsCK53M1PSgH2+zq8A+DY2Xls8qXXY1qnn5K5cmZZ5KLr0Rh76WzWwBxnMYOLDcFJZnFZCBGIy8NsGs0hW5TzEQGPAZzOOiP+ZabMNICLocR4s6xJKBK9oUPX8Thma7w1z7khrC9W5sXoee8uHFvw5/SaHySBX0Axo68D+jU8wmJVl/sL4LlM+ErVdSF14od0KVV1bOtpaIIAgefcuxC0ST7H4vxM36+mSdyWRZoYDgfbhRaLzrsdYZsBpu1D22jSO2CylCaVME0AwGOBiwLsMUi8/rSwT+/CNLGeKyQQuoCAf591hF7cAIrqFTspXsrKUlhW5/1pLWwasZe1PGxQuLY0yhUUg1hbyIGQtgiSQb2SPtiKQhygqygY4RKK3bymy1plfZ2yjdYK9NSeBykYQOgKmJtUY8WTB4U4kSYCZwtUIPUxRhEHuA0bu1ALn8eTX/5YPC5hrmhERO6gaBiP0ryGHkkillwx77+SXLHc4EjLjRV6gsOqqWyp1UsmfImwR/m84nRDNJzojep5eCKZnjOEJlYficZJEgnvS2NsQ6ebzmQk34EWNywDolUVn1L0ZIs29YWPdmoDkiIzGwz4A0K4mB6QADV6vZ6wpifWxCwg3IROO+y6ehOmp3/7XwwUdDItSF8OGhbbNCamIXa8FsSDqKJoNH1Pr6GRZ6fFC1YT+9uBY5r5HQ3eyFJxy5Cr7SNLravhT/dUVZ3wW/0sgH87LVVtqk/91Ieqat0474xus94IPqvq+efGoXrUrTfUlnXX7bwfcL9+RIlVaB1c5t93S5/HPXdEPxwp57nDy+7Zp6PxxdE9/zBptRqbF9bNOa0fyAN6cDo6bBrta7mjbZvjruHevisNbi8oPR0dsfagY37k6sdS/dgrqq0uu2mW9I+jEds8U251/yYYmy17+/bOajoVSzsM2pVcR91m6pny3vMOc2eb1r18ZsjqoZmzjsuNoH2dt2RnMjorl4fNXCnoXFZPLMsl5zeTIulq94queSdtjlXrtHscHGB/4p+Out3Li2YrUD+cut3Pxsft7U2rfF6+LHDZfPfhVh0rwPO9elxWjwJ1aN2f9TZHVz3SvLzLmyX9/rh41pkoo7r67r5+7bbcAu2cNpry1ehDsVdmZv19s9M6Gqp0szJu5gcsNyhvap+Cy+ug440P2h8b7NpsNi2+eaJf2XZZqTYOg3plUC0eHbV7hfaVag27ynX9tMrP26RTbdbr3XbhwCqebX/WJ5rahjv99G5bPW1jlRw1bLVz3zyxrrhVqn+wTk66B/UbeqqQVv2yUW/pVHYHnuMyiA33qnmQu8/d9MyGyQeTd6xj4JbfMeXjYbt5XKob6u2nTy7mfu9qaBiYVvPmfbX4kV7fltyhVzpxPjd61GsPx4ftQu+iV2g183r91Dzf7NiO2y62/EDB1m2pQq9I79h2L1i90yXGkUdGF7ftxjB30fJuer07JV+6uPADFTTKoHASyNMbYVhviKfxN/hnlv3YcFzABfOUDOeXkiQ9QbEV5eyvwOvpidAgHKiF0C1ClcAbwoPpKB2Bu+VxJ6TguSPSF8hi8CfWfCgPgoVAswIC4gBTjhgeUwtzx5OAs6s52DOkwKOcnEPjl57zAmNjXvOZGgCSdGoB6oppGkg5p0MCmDg9HbeunfPIEHDr2tGHLZSXZTmEHVB8AYGkw6YtWe4Cnk3NlRPt6rSCiQFkCr1FLUxtKGzcQYL4TVjZAGgwwGxQjSn4jGBDwPHNRd/Fk8FnZoIC2k+Hgstzn1WQlNrfjV7oXcrcUfzSpMI3WXPuUolM4ucbNqOnG5QIDy/LXX5pU/s/2VyoDRRP0y1triDY1L5p4zuJsTuBszmxqSZNeUYfPbGc/n13O7ZqgdnGFGRtLC+Lpb49tpeXU2udR+qp/ZXNxXEkmP72Ll/e0aKPWNEaevyW/sylfJfLgD6RCk3W7+MD9vAwnXgd8y5wAzmsITJub+MLS044gM5soFmLupf6eUPb+DWFQnCwl1roXmvop9uRw3dCgujrDlrrswGFhIkGhXwA32MX7C9bte6zKWpNjIN4ip3ah7RXUDovo3rmcYZfOU5DLLj3glunxl5qNjXHppbPlwqKWZWLxZJZrMqyVtRIScnJRFO0worQpGl7HHXJll5DJIX4FSV5ZNZEhYRTEtN2MC/k08pW5idrHs/P3NQSHk+0tWSacq5SLhhYLxareqkilyt5mZj5crVayRcqqf2vnboJ6fIXcunzObD1pXnyVN35qoUiao2eLRcR2SsWjdXCAJXhE/WhwNL7kCMaUAPyFnAAmjePoqv8Q+H9gzny5R+PRPxTV/HY07j4n5qpfcDQL7jW9RzKPP0sr9FvrdPPBjUQNl+YZ3/rN/fxwP9iKKaDy/kPB8eWPdQQNn5j5BX69blIiIm+GwLLoXTxrwLAlnBHPlcu4AJRSkq+UMzJGJOCUc6XiSznZLmsfxeMNaJM4IEkNPCK7/9Xj8iXvE5LpF//hfr3afrxitIrPlHBv9OCbxMewXOhEXzPt0pB6dLfcFygQKNcLZUrBi7iYkWpaJpWMjQlX8SGXjDN6vccFxS2kPK6A4McLpbyZr6ilY1KUS5qFS1fMYsKLipKVc4ruX/AwOAZp/4wI4PgZVgi+Hdk8Ndy5D8Il/09gPk3eHa/52UXEOVk6P/RO394zUufURp0vP9/TzNv5A==</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_1330c51f61c44e1a9e2a0526145d6a75\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_6f65c96f779b472d80b1a08ca4e9bf96\" style=\"display:block\"></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_6f65c96f779b472d80b1a08ca4e9bf96\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_6f65c96f779b472d80b1a08ca4e9bf96\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -571,7 +550,7 @@
     {
      "data": {
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SJy5i9uB+onnDJlRdI2kVRFjOV8UGeDnkXLd0ZdyfZ0ThPKveK9/nDwWW4JfScQBpc39pdzjf3a/vDC+tL7M9MfQEyJNpaioxz1Q3JuSI3qYGkWaWGDY31DmTb3kto9QMfEM73XKMAsEt97h5WxeOef2eO5B0A/wLQbLmeFPxY+K7haW2ChG2CqG0LKfjDpqTfauj8ISJR3iA77g4l7czGM7Xy/CQkEF1gsjQhA/RM3b4aCkf1NlVKacz2KNFalq4jdWhLYycsm87gjAHhtMd4Lx/q5+glMK6dACGOaU2AivnLXISR+fyDEEeiL+sH6gnOod8ClHYE8kdQQ1kY5Oj7am4K+Dv6uH5MFaYfguPVYcERucEDy3dS+sn3CtgsHlqJbjyUZogAD51nC9zeV2VlU4EGs8Vb5gPANaf6PqNdWp98kmRXUHPsJNdjiBotuUBO9wPSut2io4qOmj+yJwp/9aLZuRZvI4yWfce61Rxj0joFvg3R1Lj5g4OphZ+DgUTDpEKW6nVisOmqm8xIsh8XJxLjhouF5fWYbWvqmzK1VCrqclO5b29k3fomgp3Oi2UGVBpVAitWY9fsfCsfZ2Cv/Wueul4qw23mu6c5+Iw7kfQSiuVJdsN66C8a0MZBzbVavuYSRSd/9SoLA5RR+fFnKZSXyu1dVOvrSjOjaqrmzpdVb5sdujppk5LK3V0Y9TRTbWOoZBQQ81SatYhG9Vzb3VR3NeNrpSbOqU8qCyhylirvwxDeCcMIvJXPilxJzWAaZbE70jFYn4V5WfeCoHTX9ZeQnZC/1tMXS17iQu89m9qYg9qDRuvrGnPCVtIz4+KtdElaxlGv10wee6Xsg66V9af/oRuKu5NqMGQxtUqlCqTarKjbqUddf+wo3/Y0Ro7evHx7OjBfsbTrTCe7h/G83dtPC8+gvGk/8vBsOJGFZIpcYpGRN6Lz+p+JSkDDbopyNHkS9mChapIW0HpgVEy3HKyOzImR+5Ki7R6RDCXBNua8BKXtfC+g7uqYw50nizdSESDDzQAIwwZ3vLhTgwn7GWsuwosVSC4zxmvr0F8/hFTL+SOXMT4i3uUrgDyQB1uLxQTJUW3JSzDbThyLLPeCxMH59J1mEkx748WWRGLw4xIEf+HDrpXOMXivKkhcUpv8sDwjYK0VaMoqO2Yz++bKt2lEKjxm0L6OM5fsMlSu12ueN3iUg5DmbV2D/M8qBf763CdqvD09qcch36T8fT6FV8/F/QmpeOuceJr13jJiMec79I2/eIeMiRwZFjzER0F83n/YEgXxWKROlwa1Foef397FYGeJ4sQvhpV5O7xKnK3l4rs9Fd1HZERapWkXMPHKYmC+PtREnHPmh4fbVnGMGeLs1MELa9qFyiLKzExni/dE1RswcLQZeQ/gwmnX7kM6Ae3dlO9SzGIKFVpXU5fUWEFP5w+paygsRU/scBXkKYXeOarkbZ0Mao9MYLmK5F7wOLVwF/Tm4HpbF7cDVwBnSVelOJm8x+SYMECAFm8gjFgXkUfDNfLJF6RJLtr2MHSW5B2QlD7g2iBV7zQPTcgJN9u7kGg3RYXkLZ/jeMlEnD3RMTbxtr0IuQUPBOK2V9tbCFu1hyqqN/MvHDWuPWShlYues2f3cvLxNvVRpwKlCnlLbEfKQZeQUtcT0sPEqDMwD2whbIw/L0bSQZX5ETvSUbhmFi1y4vPutD5Hb/fQVl5rT+7Lw38W3qBY2dAljjOQn1FhSvo/RivJHKbvciVdjGE4XfelITy5g6+qkTTX2pHH7htwfX3JfQLCkNDanTpPMSvyvo5TREahJdfvcaOhNJfSfE0GYrdlPyU3raNcE7HhRpIdyYbsVCH2dVH0lBkU9HgtW0uXZZtOJ0hDq7lRmyixrEMtUs0xXGU7W+/Z4jWRztVXT9qmiQh9whxZbNRZKGsjtWdkM9apP6ntxdvKUbIdZzPQd8+uw9Q/6j6mfH4eCLVdhcnuutaRCgNzGEU9Bkb5MUkgmaYWEHYJ3gDO8pMGhEso6JzLc9ntCWTpebnPYztLpIzt7/5Xqr/YpVSRs0kzoA8DpvtE8cnC9tI2zAuc41KcNg3FpMo9mNvjTNpatt6vOY/WIPLjnWtChuUU5XJHtp5oHrQoKrfPVolCvSdvUsC3UOFStDTYvSvBhJaUAcDY79YG7KxvashFdXNlbIdU61EJVa0UlNlufcXlB87RBgoSMaYwdxToFOpHi08aYVJlPa2WbUDkAZensYbklZ78B80RygK6MxCL02/C9KsAx4LeLrRPEZR8mcYcs/pIeEhDsZuOazdJspnQWxX8WvOlKb42kqCxPo+1cZnSWzpFmetLE6j8ebfo8/uiz6yvXyjbeDBBzBKrFUVSl/OkHolQ+ZRVMtmj2nYWq7YilOWEgdge8Sqs72NKm5XA0gzspLXNsyiYOJkKLukZl9ZtiYnpjePlBNDzuVE30WxtcxqMXGAKjGJ7EoxcQBdTHlyeYaF/mrfmd3g7SopNxCFhaiTLyNalt0MA6eQ8wzGG3yohD5XQW4zxWEvyQIAOlDVBclYUhH60HSrGvBAXamu3rbMr5jQKSndW4oLVcT/9SaHsecrvLgBByICw6XQjZZJKHq/egxyedDFKE4IFD7ikMspFuOPSOnQjXR27n8s2YE4u3R2Ha/6hqEWD/LjpTadAT1T7+IlNPIKgVqDH1bcjCv852Xvw3pMScgDp0o816j83nD5vAy6U+WBYM9hoEA2DgRSNuvptmvIoZ3c7hqyePe2veiunClqZThQJZMg2ZMsS4IpGPOGTVu4JTetFoibx/jS1scL8HGKRsutgdRP6gWUGM3/V4yPZ7V1Ijy7EIA53z6i83l6MnkeN8txjPyZpsoOVnbC9hKIIMwk8j0MVFhPkWpPdB40Q/7xeMDBqMwD3oelAT0sXFeefmiE9o7WlSlxDyx/xOkjxqEFzSoXMwdQw3PSNeubF/RdNzAc2kiHASDcOEVj/dheYIee4pNeQbR4FgbAzCvlULC05BPBtOiVENdn94ISn6q0c9I09gKCemNcERIxAMnnr9gJkru4Ikwn4ahLPBykQ+cI2gQq59oAL8dazVMklLZ8UBtrQf1cLkON1A5hsiWcHF9o30UhOpaiXb61i13Nz1e5Z4GKBg1SNC2ahcH3RtvpDND/cjtdstQuifiQGlqqnvD4bUlZCiFwbcGDU7Mb+Z49w16UR5N21MCJvqtE0fP5OhSVF7TrNLmY3+7SY040R8DtPDvlKXGjnpqaZ6fFyx0Cila5JQqj35QHObJ4ZcKC5AIJvig4VK6nxenZHItmFHj0q4IpXpUso7KcApd9l5C3hvdZ6pYdJD2god+2pYhEHo7qFhxK4S72ICaNwOUF4NKKRB++KvGtCvrS0KlKUB46E74atItYvoIgU+NBHIkcSynobbVT6WiYgBq6L3TLUFrsa2Fau16BmSGY/3USL19zz9R8jFDZHYC3nfgvN+z6DL4eTlMb74PIj993fHILMwxaKgVSLuevgMHXrpTNPXoxrnUsF4VfdxWnvv+zygGe+G/XabZkMUCtoEqq1S/uzNL05eZHOu2jj5QkKTvP39B9aEUOMpZWd5Wgq1cHsRX0L7VKNK3PpSc1zksXnHzYkq75CswH0fTWWWxXNBNds0Q9lGsob0L70tiSVZ1qx4Iz9DFaoHRxlexhKUtojL/3Xja7kfRW7zF8UwrxA/WiYa5WS0R/gbmNN42E0IAyfaX0s/sKxdv6KxyCtLBIyKf1pfItVja/NUYLAcxu6KSxlaNL1sg0HsjWSqu4FLcQH1jB5cBDVanb0iSyOmihVrJmcMsvkAt9ts4skWe3OUGyAK2v84HScuksicPwqeKT3VOjayoBnzOkHLSsKbnxbgN8yNXGK5a8KLO3ehnyRJqW822UxX8JyPvGvQEdaIbx7B2kRMQDJSoIbtVD72V5LuN1SjUDZarH0MTlR7jPFCQnbzjF0Bar3N9aVvHlZ2VME5iGE9D5dJR1S/5YLw38xhGRbpDUZnFVgFWXQeenIa9vtWsS881Hec32KZQKupo9thateOk7ppt00yw9EF2xhlYqI9O8l91F0AMfu4vA5lAXxMw3TyplVwu45sqc5mQPRdBFLQUnwDUCiKGehx4T8cVCWU6ZqSZLPsoJmJG/EVECDZunm9DplWJFd4CeLvNxYTnWF18oIpOBjzRgNvWSOFYniJq0+NYHW7nvVoPhs2Nt5rjf0rFaLVF1TXcMkzcjm0YO9ir6qKroqsoqEtlWtNTPeUt9IybnNU31c95UObTcVt8YZvMab7Tf1reVWC/+wMb6+UMaqzy8PKCtfn5AWxWL43blEZC9zFfMPALdeu1pYvYyMDsZoXG6XTb0lThq8EhLKvDL5rR8KgYBHm8crwuryAqd6DeqXt+Wb4Ct58J8s0ceAFUtj/0MM4h/isFyTRt33IesV6Z03YnpZz9jZakvZpmPFKknqnbJpOIgVglNfxfAfEPIXrr6/oaQ0KSrYnz0wgzKLQe2fBJm3s84xWC4zQ5LKbhCbH69/HMy90Bx9JBo8RZ1yWlv4tsLRT6jrb17U+vpyzIpA+K+do1dPjUAq4Fw3wHf4Z5T9vx+OhlRITTRD0KXObqw+rtq1z63+rr+KaWeVbCLt9mUrrNR2VW+HqknGE1H/SrrcWa1d1bkaFdFLqoqEkQPqkh7d0UMi5kyif3nwOUPj1t6KU8q7/VILu2YM4r7sxz3wxCudazp8JfamNfQJKRAK3FcFmyWCvubvtzzMUszzWWL9jJcV7vy0jS4JafsAZetsiZm2kVR14DGAEbpwloWqPohIs/5sz0f77pa9XYh+aJRw+ZXFrzgQCLFAJeHi2VQljhRr2cqX6G3/yV6pWv01EvwytfmlfIZC5m+W6z6YrzyvUj0QQn2bgM3/njXBGgJbok4lR5aAs3hVlc7GZsfLHrELtLKIzzuzFvZVVDF6Z1asCWNOLI2t53OaECWlbCSxxpEeHtHW505l9YVzEdNzMDoHM9DehjYvmUqXQlbf9rIILHKcxQ6JNYqn5nb3c7A3iu+uyt8XVXndYYF0kZabbQjGwd1Z8PyQ15KJ6jVDxmkSjlkmIoNyAfaovOPaBZK15hjzldhaS6+p8ZX1VqQlZ0jnqTXvSvvXS6BGQ+/WBiUxy4AU818OdOl+7zAEonKipsGtmT5ppJ+7ebtErQIrrQHdTwXs1jXrYNTtr/PSCRfrFMuWtF31wSoTpd0g7DV9YFZgN+PSrD6/su0gsd6/uUqIQy/qhVVW5bytizf4c/P+N7zxwI0n+i0nMT8OMN1+qemRITeTg62TeqFZTcBdQFexXH2feyTRrNzE6cZzDbnUdoRAShxIyN8nJwdg+ccrLKLs+MsISSdgQloJ+uofUMScnGGG9wtusPq/HAehz6+5nEdAeXDizMqp4szurBkoedwfji7IbN3UIdDI851Fi8WIaIeUySVPL3t49qbTmEWfHjxRZhN5Gw7ijOaaV+89TYdKgQLmAcIhQzO0aMCUIDwy+Mbg1azgqr1z3/8b6fjWP/3/+D/l//8x3/SR37/+Y//gN9X1q8kiU8HOwoU2eIXl5BcSwpNNpDiE/9QzcMz4qCE/vUMlQoUozYfMnHnJ88TDXqdq6KGrL2scXjxSlh3pgydTkfwbVQEqiW8mUH1w2BGu8FxPMtI1k4Bx1seXuTPZbGuRPWRfUN1nOThkDl9IqBWXScMrAM8vI7jSDypwUlkKx5wwJFWeUikYWdkuUJXBemQJAGBgVOIh00K95W9iPBvr3/4vkMnzw0k2OFnwnV6rO52UxkngBqi5G+KWGof65jew2DsFo9p4DixowOKuohmVKp0WNMub9MY9Of+EPdDHp5ah89ZyC2/VAdmDBgohDmy5dHF/2bH+gad1mOWjtu42EsINMh42LIOpfcPkOKT6p/zHJw+s4DQvAdihvakAaX1P796Cv8onnSHAmTdH3oO/HJhKnJYHJ2FlMv7Qzw1gNgAAbm0t9GvAGU5p9YAU+mRAUiFvnwIGg+fBtsr+Hyn0MIU7cSRSFZvIaIciToV9xVhSWBLsRwcNQ6X3ib/zIOd8J3Nsg7zq6FzkPyG6DxFPI+MXFyeDNmu/B59t9Xt9dnXvku/DkeQM8R/9OsYPo7wBVr29WQMwA4+lceet3Xwe+8E/nPZa7YuJozwPcYBS+hCQtfpQcIJeze2j+cDsIRuj0E4XTwhQP9jhQwR/QSf73McmuDiOYKRQ1MxYTRAlrGkMcXo4cduL/+OxE76OTiWOMRKD+nLtD2g1keKrnt1hYqg7KAHEY2YVlUFopm2iFbjj+8dChyqMqirhxgoqQW7VEHopR5U1UFpCkUsntNCbRMJQvl2lXG1m41/jywYvVQ4lrm92soDCh884COYipJ5KtkyzQbXGa6/W8/p9ppT69nLnyyHE/tikU3MpTAG0GBRrwwGMjb5PrVwsejQCnx0FpLrYnybT7vdYW8wP3H6/eG8f+I40/6UDAeuQ6aDaY+zt8uHyJ2FxqXTwfvX5X+gW/RVlnM+ODVN/D+U8a476nk9MhgOur2+63ge6fmj7og4jus4o9nDGXcFl2v6Us1HYdL1+sPuvDuejvxx3+lPx9PueN4feP3B4MTpDlwjk/8dHEAYcgb1LmC/R33AXk84gd0T7gbCcIjypK6g+4cv+Icv+IcvKPy3wS9f/+1l7+3TH94OFvFfF0dPnh/3Xs6e/vLq+Wjo/rg4fnn76vnLwZNfv3ne/3Pce3G8ePrr+5cvnsR3b3svlgvn+NvvR0cvf1y4f34+fh//7afjR/qPyj+jL+miL9mymFM5qHUq3bJT6T7IqdzpoPbMtHrbj+eOtmHYGpyA9RyNhs6w61DPLHdPDVm7vNXuSc91+sP+GAyFMx6dlLxXA80/nNn/Js5sT3VmezvLaFm7CnH/8Jg/ksc8OBl3T4ajse/1Peie4+l0OvSng27f82e9+fyk8OmQ3gV7CBJ8hCXGp68Vbk/BKWp0FN/t9MZLGxfMi+mYvDqKc0odQOLjdVpNaqBDXKT+1KM/YOSgdrT038axtHjx+ziYhiyjLEytCuCht0oxk/qpBsXQaHxq9FX/P/Vpa+VRrdC/sQvMp3kwzxv3uz2371r0va1xtzcaDwf4udsduaMxT+86o+5g2KOfT9wxGCKwKWJtAIiMumC+BhYFHg0H/f5gyBAH3QFMmSjBkwHYhWGPpgvLqBBxTsYj1xn0EWAwAKZ63SErvd/vj1yXpp8A6a7DCaLxHFtXYCnYioMyP60QrGHc+GC/nV2/8a1P7/4rXM3hfO6441HP92b9/slsOHZG465D5t3RCQw/vTF1uvaew8vAO+fNMvDO+asMvHNgvJrwuUg8S2D6YZyNiAkDCpYDcXBtinDYyWV/jdmHJdRnYUwXycX3zgwTaDH0zY9JkZMQGPJn5K9BdtNQ0HOi88Rb8BN42srjc/71aw6BdRDQyuqVIIz5hsO7uSZoh3fZa6LidF1OGb7yNc+nd98CcYGN1+pP2Du08TqZkef02ooKGX6Klu3QOrI0dH4pjCyWnFpnHiSpKJvWDBCK3GJ2tlVbwijkXZOx/weemIEp</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_1330c51f61c44e1a9e2a0526145d6a75\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -672,7 +651,7 @@
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type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_0e35dfa874a945d3854623c6423e6815\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >eNrtWQtT27gW/itqOndJLsTkHRIgc52QFy1QCC2U3Z2sbMu2iC0bW4kJO/z3e2Q7bwPtLt3uC2aaIB2d9zn6jnrg86lFGhL3CPFVxyVDz3E4+hW5jk85dVgdecTCnE7IPtIdxrM6tqk1rSPbYY7vYhXWA5Nykg3/qCPXgxWL+jwbss7yqQurzGGwrGB1ZHjOmGlZ1bEcrx4d3UfxX4oFBMCPatysI51yIGOcML6PbMqy8Xo+l/sP8HLusz59oMyAc46nES8LS/vIxZoGi1mL6LyOCqoptGEkaxJqmLCSl8pCHuOYgnFz/vGX7IT6VKEW5WAiHnNnTpuljHuU+VQVYkm0G9v1eLAb+fFg7sesN2Yg04M1X/Woy5FwxOEWdl2Lqli4dtdRORFu8gi2txrpdOawAZ4HeT5HGtGZjw4RN6kvGYRfQFhOHY2kM5Lp+FwK98E0wtHQJUyYLKuCqzj0489JOz3MNIvANhtb1n4kQQI1B47DYDUdON4og5Z1cK5gSWytLHOqikWXeLrj2ZipRGJOkM6EiQAC0hs7KBsdOkDFQgb4UB2l17SWLMIMbqLDQ5QTJM+q7hE+9hj4HRHLJwvFzDETmq2z9k2qc6FfSCC+PMLvExLSkH5McwLJI3dj4nOZUTsMV8fDNklHPskIHvsbgtyxb0Zu3E+wcSbiMDLjGSu/XAehRRRI7hiGFZXvMCwxyFZX8BIrxOI7iEwgweNICu3Cv6URmQqnp7yUUCgmllQL+/57qOKYbzo15zm0IQ1TM+GPGfAnpH+Y442D3aQC0OgEhQwPU6t9JoU4VsBScn+YyqWgdD2+SeIwUBGcwWDruWJI9kBanJnZnoJijPpd2HCGWFE8MgnzJ+w/byt7BZzLgVUxgerYNhxcosDhjzB+jQTXmcPTddOZEC+TQB+T+6KJGMsMi/lyviwIwArieUQbutAGielY0NOWCaviN+7CoRl1RDmGbiIO60COFTCfgV+e6N+g8wrZMPIYUGvUB6HTWZ9eJ0QNZGGFWPW6QqCyyZJWaviznygvasHZvOjBce8G385lURY2ZsVyRNN/Umbo0U3JGvZGPsEGZAvbPB0GY23JxH66EfJsJPohCqBqEnVEtEwG/Tez0EEcTT40o1/RMLxL6mjrp0JZUbe+p3qrh55UsvIHKCniKASPPV8E0HXgRiVeglzqv57YsBRCQdmwB/hP5fjrSF2Yx8k935QiUX+oU8/nQ4cNRfonlNZzpSQVyqKaEkOFfrf6UcTXVRRW2dgzAANFaoQF/fg7pUE/dKfKmHMAH0kNaLGdlLQplFqjAkcCAk0m/onkS1pqDcBunWDICootNJjaimP56GzMhb0aakUn4dOdQmFkA6KMAIxGndeG+8UMYSdmHI5T7BNtDmHfkpz43d9M8+h0CB1zUo3Y61ZG9ZFgRXK7W5yUAuwPVbgHwLHz81jnK7fHrE8/J3PtzKrIZdejCfbS2ayGOc5iBoENwUlmeVkIEYjLw2yWzSFblPcRAY8BnM46Y/51psw1gMBQor1Z1SQUid5Q23U8jtkGb8VzRoQNxcqiGb3s3aVjS/6chflREvgFFNOGKqBfzSMsVnV1vgCeq4Sv1F2XSie+SFdKVcWWmoYhCBB43r0PQZvkcyzOz/X9ZprEY1mkieZwsF1osey8uzG2GGDaIYyNOr0HJitlsheWCQB4LHBRgD0GhTecNfZZLHQdq/liAqELCPjX+UToxQOg6F6xk+KlbE4K2+piPq2HQyP2soaHNQphS6N8sawRYwc5kNIGQTlQr6KaO1GKA3QVDSNcQrGbN3TZ6Kyv07bRRoOe2fMoBSakroC5ST1WXHnQiBNpInC2RAVSn2IUcYBoWNiFXvgynvz6y+JpCQtFIyJyD01De5LmNfRIErHiisX8leSK1QFHWh2s0DMc1k1lK6NeMuGXCHuSzyu+boiBE72RPQ9PJd1zbBhi1bEYnCRR8L40wRZMuulMRvIdGHHDNiBGVfEpRVe2GFO/8NJObUFRZOYPA75JCBevByRArcFgIKwZiDXxFhBuwqQdTl2DKVPTv/wvBgoqmTWkrwcNy2MaE68hVrwWxA9RJTFo+p5aR2PPSosbrC72dwNH1wv7CtyRldKOlqt1Twy5KYc//XNZdsJvzYsA/u11ZLktP/fTtGXZGDnvtH672Qo+y/Ll59axfNJvtuSOcd/vvTe53zyhxCh2jq4L7/uVz5OBO6YfTsqX+ePr/sWnk8nVyQP/MO10WttXxuiSNo9yJj06Hx+3te5trqfs6pO+5t69q5h3V5Sej09Y1+zpH7n8sdI89Upyp89G7Yr6cTxm2xflO9UfBRO9Y+3e3RttZ89QjoPuXr4n7zL5ovze847zF9vGQ+5Cy8nHet44rbaC7m3ByDnT8UW1arfzlaB3XTszDJdcjqYl0lceyqrinXU5lo3z/mlwhP2pfz7u96+v2p1A/nDu9j9rH3d3t43qZfW6yHP6uw938qQMPN/Lp1X5JJBt4+FisD2+GZD29X1Br6gPp6WL3rQ8bsrvHpq3bsct0t55q527GX8oDapMb75v9zontky39ybtgsnyZnVb+RRc3wY9b3LU/dhit3q7bfDtM/XGsqrlWus4aO6ZtdLJSXdQ7N7Iht0v3zbPa/yyS3q1drPZ7xaPjNLF7md1qshdiOmnd7vyeRfL5KRlyb2H9plxw41K84NxdtY/ao7oeZl0mtetZkelOdf0HJdBbrg37aP8Q3400Fs6N6fvWE/DHb+n507tbvu00tTku0+fXMz9wY2taZjWCvpDrfSR3t5VXNurnDmfWwPqde3Jcbc4uBoUO+2C2jzXL7d7luN2Sx0/KGPjrrJHb8jg1HKvWLPXJ9qJR8ZXd92Wnb/qeKPB4L5cqFxd+YEMGmVQ+BLI01thWm+Jq/EX+Gde/VhzXMAFi5IM3y8lSXqGYieq2Z+B1/MvQmb4oBZCtwhVAm9ID6aidATuVp87oQQvHVG+QBaDP7HmQ3sQLASaFRAQB5hyxPCEGpg7ngScXcXBniYFHuXkEga/9IIXGBvzWrypASBJp5agrnhNAymX1CaAidOz59aNcx6xAbduHH3cQYVcLhfCDmi+gEDS4dCWLHcJz6YWyolxddbBxANkCr1FHUwtaGzcQYL4TdjZAGgwwGzQjSn4jGBNwPHtZd/FL4MvvAkKaD97FFx991kHSanGQXRDH1DmjuObJhXeyYpzn0pkEl/fsBld3aBEeHhV7upNm2r8YHGhNlA8T7eyuYZgUw3dwvcSY/cCZ3NiUUWa8Yw+BmI5/evBbmzVErOtGcjaWl0WS0NrYq0upzYmj9Rz+2uby8+RYPrb+0J1X4k+YkXr6Oko/Z6gfJdgwJxIhSab8fiAPWynE8OxmAK3kMNaouIOt76y5YQP0JktNB9RD1M/bilbP6dQCA4OU0vTax39cDd2+H5IEH3dRxtzNqCQsNCgkZvwPXZBY9WqTZ/NUGtiHsSv2KkGlH0ZpQs51Mw8zfAb52mIBQ+/IOpUO0zNX80rOa1ULanVQomUSvlSRVGrBJJvT1FrxcJerbomNOm1Pc66ZEtvIZNC/IqSPDIfokLCGYluOZgXC+nyTuYHY5HPL0RqBY8n2qrmNb2S04uVUrFcKueLNa1YrtWqWFfLlWKxoqYa37p0E8rlT+TSl2tg52vr5Lm+800bRTQavdguIrJXbBrrjQE6wyfqQ4OlDyFHZFIN6hZwAFoMj2Kq/E3p/Tdz5Jd/PJHxz4Xiqatx+T81Uw3A0F8Q1s0ayjx/LW/Q72zSzx9qIG2+ss7+0nfu04n/1VAs+BeK/TFpEbyUFsF3hGJ5wGKVvyAWywMcUbUyUQtlXMrjyl4Fqzhf1Pe0oq5XcfF7YrHiDiq/Lhqr6YW8XiyWCwopl3KFoqLs5UuFSpEQpVJTq8o/AI294NS/DR4LvgxGBP/isT+XI/9BeOw1q+wvfe1+z2AXEOXE9n9rzB9fM+hzSo1OGv8HImUd8Q==</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_0e35dfa874a945d3854623c6423e6815\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_736a46b5462b429b9b1259685d90555c\" style=\"display:block\"></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_736a46b5462b429b9b1259685d90555c\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_736a46b5462b429b9b1259685d90555c\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -684,7 +663,7 @@
     {
      "data": {
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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_0e35dfa874a945d3854623c6423e6815\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" >eNrtfet62ziy4H8/Bdt9oRRLMqmLZVm258ulM52zne5s0j0zvT7+HEqEZCYUqSYpR26P/u+8xzkPsK+wjzJPslW4kAAIUrKTPvPt6TgXS0BVoVAooAqF26kf3FhpdhuSs30/SJehd3tiRXFE9q3AP9ufxcmVT2YkSYh/Rfxe77jn+F1/SPrD0XDkTIbd40mv7wynxyO/u39+mi69CP5HeucdL0m825vgt6tpHGVeEJHEurM+XAcZaQPclGBBycILx9bGMgF3gmgWA8oMUtozbxGEwNsijmKKPbamcRgnJ9aXHv0ZWwsvmQdRexJnWbw4sZxOd0AWY7XEZULqiwui5Sq7yG6XIJDEi+Zk/xJYuCFJFky9sO2FwTwCLgLfD4HSLAgzAjzMgVoK+aThNq0Yigqy24bTGTTvXdjJdXxDBVUmfT960WoxIQkQjOKscTKLp6u0CWQnceKTpJ14frBKT6zecv1xJNlnyjSSF20ypD9jXtyJ5S7XVhqHgV9k1ZTaSQGSJKmuL3WtR1nIgiXgKIo8tpZxGmRBDM3mTYCHVQZpE2/6fp7Eq8hvc5ZpQSaGJyHAAhXP94NozvRqeo1kgwhaqE1uSJSlorAPgZ9dn0DrZW1kDrLGFnI2C+MPJ9ZNkAYTVJxytX5rB5FP1lCy4zj1tZzE6x1rGa/b6bXnY9EO/YPVohVq8YQuJPCqmyuU8zWqYWsaBtP3vpd592mxMPZQolcLkqbenEjaI3r05vSQjSWnWUJIOo2XpJ2sovY1SSAtnSbBMrOobtrecgk8eCiBw3iakaydAo63sM/38AeKTTNLcGGdWY1G0zo7t+72LAv+zlbRFFEtn6QkCaCX/0Z+BmkcN7DTAIBlJSRbJZFFUx8jnc4siRcNL4snANSyGgtKcNGZxj55haJ8nDWcZnMM2Ju96mKegxiyXrcoiLE6uc1ICnw+qDxBZIa0kUpEPli8IEqrQcl3JqsZjO0chVeQ4Wzj+kX0X8IzLeZeHDOWQ5JZT1GZFt7y9Z+fPAPNHOu1mZPsKShjEK3iVUqBGzdeuCItpoaAiWiihkhx4qXkivaGlhXPZinJGB/BzGKo1umZ5QgMS4KH6jhjnsowi5SNRcKUSETOzyy3gojMWSck0Ty7ttpWt0Ta7ajEBTEm4mmW5hRZkY+shpm02xyb+HjpZdcdkDvILCfWLHFRlPO15XJ+pJZOtApdFEVcXjiXyJQLLDByTeuAk7eqkKwDy+WIcuuwwuZ1hbkPLcw1FzapK6z70MK6emFc/y+SljVvWZNLc6e9jcB1mj5OpmkQXb8mQL3By3tPbumY/xeu9mGwfOmBbifeh5dBxH7jd07iz95SqGVOPc08sGdv0D/xeRENwMi8VaHBqNlfBOnzIALD0KBZf/87UyEwVY110zpEBOvUckm7X+DlFVwLzdK0OQegtFLwzJDYI0rsUQ6DPxQgjOeNcqkHHPvXJING4d+W8YfGmgG0rG6zmev2RtLivO8rcrTOlDEA85k8tQxN/KgxTP6VldXhGVkTZzpkXmslQ4yitJUF1MJbN0S7c4aa44qanuYQOZf/ol+s20FrYZ+xzva0lifrZSNXAVUG0A0L9QbdycFE1fKmz8sAUYmSDllHVsVyqAmOGQ8QLIy1gGvqMnnXw94xllRHYGmqkxODHuM6qgrkwyt29SXBURa1HexuXWc95DQFLd6zBBGjNfno1iqUXkiy+GpSVZ0QytUnUbwIIvAxklyHg6ghqYCp2trQx0VAWZBGu9YWKqKjILrSbApTWtupDBsbkAt/p2aTyBUmtmQFsvgNOtJvsgQ8bmbrVd8tNyLCdCkm5m0ynzS+uks21ld3c/xvsmm+NZob9O8TLwW3av6wEiUInI5EMKu5BYhO1+0eQf9MYIjuDN1BFz7P8bMz7OLnSTFIFWjnlts9LmTPK2PTCZBtVGkBQicxtiJQraIwPw9feRlMmCLwgaA94O9tC6aJNCk3lNDIDVTTgDp88OtUgHAnC9IODpqah5bEHwCeA14El0JBcnLvGLl3QA5gc1LvZFLc+MQfLt5dyqlQSLbuIP+vyTRroHfxDniHX0HLcluSz1do5KakWoxRP5gHGXWeXyXBwkuwqS4orP3ljP7YLfjozobDSZ9+nM2GM4fQj92p53Sn9KN/1B12j+nHUf9oOPHpx+Pp4Kg/sVucIOkNh9MuzZlMJ36XfXSHEzKd2QBDxaTz9YZAiq9yNpzhH4rtkemQHHPOJpMh5+HYnx17PHV0PDqiH6eDieMP2Mf+aDrq55zNhpMjn7HjT/zJMWN/RHyPDHLO9nLupiQM38AsClgajlmGNmmBmcksmJfmLD6MOD9G5CngixGO6h40Mp22tCw+hQlSGNkCv5jLMIKtfEAXCsGdMwotaSFXENqFgUs7fh9OrxuDwdcYNGja4z2DIkFR0BGHlBv2Af82x/U0h45Gs9SxOF10gnPa7MtFrqcXTssq/l62lAyXprrljE+CcdkszeNUoXcwQgFmAKo8zSectt7lp9ztV7yXEikBpVibIP3B+4FNHptyRy9JXB747tt8BnJoFLqDAZUH/G5KlB/UiCBiVxKxeynb38pmyRvGLTVlPVZVWZdNzQUvpubQhi+gHWAic6sImrUgNhXIxRBYgPl4yyq1XVmkJjP9qZqr1jJ/fMsxEbv3b56HZRnLqm+59oOazvlDNF0hUVMjVHWf+v5Y31XdLZ1ObyJuuHyc+HIbxoe8servCLhzjIJpfs9D++l9m/u+Df7gJn9wo9d2sG2ZrtT/3HthVmdeyl5n0Xvz5jy1nHs3p/PHa06nTu7Og5vz3mQNzSk3XeG2iAZuqm2bz/Hv73sYCyyRUPLwR1UBgzYJTiWVUppu82B90WZZ2xxJy156MFnIeHTYLoXfPsqjVHuVyL6gY+2l7HZyEOBnBWyAaSX+Z+/z03ufuiGsiJuYZJSHbVo0btOSAjf3b4KdlZPOvSdxpV7iJPaVl2Tpk9tnCJpPzKlc5AkXym94WU6BZuu2rJ45BwQ72ApxxCH6+LsHCrQVcsAhKUb/IRg9/D34OEz4fYT6Usw+5RhzEN3gPB4EOvOgleTOyrzgry3X+kKLRxbeU46dJSuyRQsjMvey4IbkS4inxQqngFl4c3C3V76yBKH7bFK8Bld9c5wOjsd0KG520mUYZA3b1lw9hiQWK09LisVzTE4Dgl4tERbK1PEuFMKX8hiPUl4mdIfEVUKWxMvSq3iGq9WrMFTsuCHwp5AdWwcHgW7zeA9PM+CmZaWBTzgPnEvGshQQLMkQAH+g23S4dAC2qQLnkqNDd7kyOk/lYcUQXGMi09iydhNVhb2uK5cHG+9TKgXVijX6B3QsLHkHhQXhjVNYkVJryZ8rPQTNGRL9qVnvtZStaMlVqjJ+0rh+oVXvwuz7FZM8Y4bBIbysqK6hgeX6Gwce2S2Sg10M+LOn8Sk9DeNekZ3FsLt4K5ZS0I1gS3GPI/9F5AdTkjb0SHbA0vFDCnpCUbUNSGJAv+BDAh2ExVoSIFkwEpiwhWZBzoWNfox9ST0ZsDok8UJbVjZaRme5Sq8FAmXUNsaeyiR1j12wnm+a4dW84KjeOkjty0vV8AngM4tDpe+D5RUdh2xtqUdi9+1XdwbwzYmaTCIfEt+aZ+O84NP7lsvwTFS34VhtU1m4MwnXq+r53qU9WOPpi2alBZEyexTxHjWyGwsvfU98K15lTftBbF6Fcfx+tSxxK9ZvrG++sb7guME8ihOcINLRsqZ1qvkqV4eparqapBn4aLTv5irIeLuia9X2pTZdFJyqqFUzxxKHq+h9FH+IFPYqvAYJTy6syi7tInvsgibRo3dn7rYdRDH32fPtXSen+ZAeUNarezUbK1JjfddW29pm9T2kor02VRMRubjTP53b2qQiDkmHJEmcNOyfGS/y2G9zO2Lc2cV3AbAC3sVBJOYeygbTx9DIb5ZkWlqlvQLnz7v9OcqC8C9sw3fDJxj7o9uTW5ZHwYTsJPtFt4cHYHVuf5ykJLmhm3f4PliSpITiiaxGA3zcJCBpvpFZtBdPv3AuO4GE+BqLBwV0ytaHb8V+6SXvcdv9mSXx2/l1RZLbN+DPTrM4eRyGDVvfui2LnlWuIS9LiKkQCbHDaIWpKgQgnYQs4hvSaJpUuSyhjh+kUIkIfQ+9MVvW3SbfKwzVSLPHEUwckMHnibcg0ibwCuIx+yC3n3BlzDu7J6sg9B/zfebPg/kq0Rp/SqMlotbbVEVl8GpX6iqLsmrekz/hPM1iFsTGQA9+k3e70l31T7yUHPULICmxBPuMhYoUUJomQ1K79RJMk05Zy5Bx8AQCBg18ug2dw0uJMuxadF8JtEiTIW8NkLdGyDQEG+AbwLUMGUeNohUoU20niOTlBmsYCV+RBLeBFAhKsibJFXlOfWxcf3ghOcCKVKuAxnvFOIXdmLZXsd1KblKwljN2zECylBSAjSn6oQdJS8q7KDTCQbSVLDuVUElUYGbXODfBcfVbZh9WUbpaLuMkAzfIp5a/Wd6uTvXuCp0ltVB2TkTTyqYstFxycYiTXL4VHhOmqySB0VpNTMmSTmIceRajhZLyvbqFynZE8OFWT2pqe8ywAGY3eflF7D/nB1k9EN/znf2UfyVxo9dTrXEWZ174NA5Trd5x+Fc8KEXr6V4WGaw6INXSNE4TQKnexn10KQAATgEshc54HBN35EJOB7wn8KnoR+piCbCCIyaywjWR61Z8fgQkD6iIoSzc6R8pxyjymjNqOZ6icLnkXscfNMmB5n5Hgvl1VhLd7a6iu72P6G4/QnS39aLjlSs+bxFdUXVJdojYrFBFbnKmGFd7gyPwj9S7xULvNtXy0QbrHYSkYUiSKhd+QeUUgfdxKZ0B2tO5nnowc0vlpbuGH09XC+h4nWlCvIx8GxL81rAZqJ0fo6JfO/QgIu79LlTzwOri8Qix+1ABv6aSzeFpe5jhJaE+pbi4TkrWmcIrp8pXUSG3YXd9yqJhkOA7runZlP/BN2YrceIiZg/uJ5o3bELVNZJWQYTlfF1sgJdDznVLV8b9eUYUzrPqvfJ9/lBgCX4hHQeQNveXdofz3f36zvDS+jLbE0NPgDyepKYS80x1Y0KO6K1rEGlmiWFzQ50x+ZbXMkrNwDe00y3HKBDcco+bt3XhmNfvuQNJN8C/FCRrjjcVPya+W1hqqxBhqxBKy3I67qA53rU6Ck+YeIAH+A6Lc3lbg+F8vQwPCRlUJ4gMTXgfPWOHjxbyQZ1tlXI6gx1apKaF21gd2sLIKfu2JQizZzjdAc77C/0EpRTWpQMwzCm1EVg5b5GTODiTZwjyQPyofqAe6xzyKURhTyR3BDWQjU2OtqfitoC/rYfnw1hh+i08Vh0SGJ0TPLR0J62fcK+AweapleDKR2mCAPjUcb7I5XVZVjoRaDxTvGE+AFh/ous31on1xRdFdgU9w052OYKg2ZZ77HDfK63bKTqq6KD5I3Om/BsvmpKn8SrKZN17qFPFPSKhW+DfHEiNmzs4mFr4ORRMOEQqbKVWKw6bqr7FiCDzcX4mOWq4XFxah9W+qrIpV0OtpiY7lff2Vt6hayrc6bRQZkClUSG0Zj1+xcKz9nUC/tb76qXjjTbcarpzlovDuB9BK61Ulmw3rL3yrg1lHFhXq+1DJlF0/lOjsjhEHZwVc5pKfa3U1nW9tqI416qurut0Vfmy3qKn6zotrdTRtVFH19U6hkJCDTVLqVmHbFTPndVFcV/XulKu65Ryr7KEKmOt/jIM4Z0wiMhf+aTEHdcAplkSvycVi/lVlJ96SwROf115CdkK/W8xdbXsBS7w2r+rid2rNWy8sqY9J2whPT8q1kaXrGUY/bbB5LmPZB10L60//QndVNybUIMhjatVKFUm1WRH3Uo76n62o5/taI0dPf90dnRvN+PpVhhP97Px/EMbz/NPYDzp/3IwrLhRhWRKnKIRkQ/is7pfScpAg24KcjT5UrZgoSrSVlC6Z5QMt5xsj4zJkbvSIq0eEcwlwbYmvMJlLbzv4LbqmAOdJ0s3EtHgAw3ACEOGt3y4Y8MJexnrtgJLFQjuc8braxCff8TUc7kjFzH+4h6lS4DcU4fbc8VESdFtCctwG44cy6z3wsTBuXQVZlLM+5NFVsTiMCNSxP+hg+4UTrE4b2pInNIb3zN8oyBt1CgKajvm8/umSncpBGr8ppA+jvPnbLLUbpcrXre4lMNQZq3twzwP6sX+KlylKjy9/SnHod9kPL1+xdevBb1x6bhrnPjaNV4y4iHnu7RNv7iHDAkcGNZ8REfBfN4/GNJ5sVikDpcGtZbH399fRaDnySKEr0YVuX24itzupCJb/VVdR2SEWiUp1/BhSqIg/nGURNyzpsdHW5YxzNni7BRBy8vaBcriSkyM50v3BBVbsDB0GflPYcLpVy4D+sGN3VTvUgwiSlVal9NXVFjB96dPKStobMVPLPAVpOkFnvlqpC1djGqPjaD5SuQOsHg18HN6MzCdzYu7gSugs8SLUtxs/mMSzFkAIIuXMAbMquiD4XqVxEuSZLcNO1h4c9JOCGp/EM3xihe65waE5NvNHQi02+IC0vZvcbxAAu6OiHjbWJtehJyCZ0Ix+8u1LcTNmkMV9dupF04bN17S0MpFr/mrO3mZeLNci1OBMqW8JXYjxcAraInraelBApQZuAe2UBaGv3MjyeCKnOg9ySgcE6t2efFZFzq/4/d7KCuv9Vd3pYF/Qy9w7AzIAsdZqK+ocAW9n+KlRG69E7nSLoYw/N6bkFDe3MFXlWj6K+3oA7ctuP6+gH5BYWhIjS6dh/hVWT+nKUKD8PKrN9iRUPpLKZ4mQ7Gbkp/Q27YRzum4UAPpzmQjFuowu/pIGopsKhq8ts2ly7INp3OEg2u5EZuocSxD7RJNcRxl8/vvGaL10U5V14+aJknIPUJc2WwUWSirY3Un5LMWqf/p7cVbihFyHedr0Lev7gLUP6p+Zjw+nki13caJ7roWEUoDcxgFfcoGeTGJoBkmVhD2Md7AjjKTRgTLqOhcy/MZbclkqfl5D2O7i+TMze++l+q/WKWUUTOJMyCPw2Z75PhkbhtpG8ZlrlEJDvvGYhLFfuyscSZNbVsP1/x7a3DZsa5VYYNyqjLZQTv3VA8aVPX7B6tEgb61d0mgO6hQCXpSjP7VQEIL6mBg7BdrQza2dzWkorq5UrZjqpWoxIpWaqos9/6C8kOHCAMFyRgzmDsKdCLVo4UnrTCJ0t40q3YA0sDLk3hN0moP/qPmCEUBnWnopen3QZp1wGMBTzeaxShK/gxD7jndJzzEwdgth7XbRPksiO0qfsOZ0hRfW0mQWN+l2vgsiS3d4qyVxWk03v579NVd0Uc2F2+1DTz4AEaJtapC6csZUq9kyDyKatnsMQ1byxVbccpS4gBsj1h1trdWxe1qAGlGlvLahlkUTJwMZZvU7EvL1uTE9OaBcmLIuZzouyi2llktJg5QJSaRXSkmDqCLKU8uz7DQX+0702u8XSXlBqKwEHXyZUTLspti4BRynsJ4gw+V0OcqyE2mOOwlWQBAB6o6JxlLKkIfmm5VA+6pK9XV25b5FRM6JaV7S3Ghivi/3uQw9nyLFzfgQERguBS60TIJRe9XD0EuD7oYxQmBwicccjnFYvwRKR26kc7O/Y8FOxBnl86u41XfMNTiQX681KYzoGfqXbyERl4hUGvw45KbcYX/vOxdWI8pCXngVInnGpXfGy6fl0F3qjwQ7DgMFMjGgUDKZj3ddg05tJPbXUMW7962F92WM0WtDAeqZBIke5xlSTABY96waQu35KbVAnGzGF/a+nQBPk7RaLk1kPpJvYASo/n/ivHxrLZOhGcXAjDn2wd0Pk9PJs/iZjmOkT/TVNnByk7YTgIRhJlEfoCBCuspUu2xzoNmyD8dDzgYlXnA+7A0oPuF68rTD43QztG6MiXugeWPOH3COLSgWeVi5gBqeE66Zn39kr7rBoZDG+kwAIQbp2isH9sL7NATfNIriOZPwwCYea0cCpaWfCKYFr0W4vrqTlDiU5V2TprGXkBQb40rQiIGIPn8FTtBchdXhOkkHHWJh4N06BxBm0DlXBvg5VireYqE0pYPamMtqJ/LZaiR2iJMtoST4wvtOy9Ex1K0y7e2sav5+Sr3LFDRoEGKpkWzMPjeaDudAfpfbqdLFtolER9TQ0vVEx6/LSlLIQSuLXhwanot37Nn2IvyYNKOGjjRd5Uoej5bhaLygnadJhfz2216zInmCLidZ6s8JW7UU1Oz7KR4uUNA0Sq3RGH0m/IgRxYvTViQXCDBFwWHyvWkOD2bY9GMAo9+VTDFq5JlVJZT4LLvEvLG8D5L3bKDpAc09Nu2FJHIw1HdgkMp3MUexKQRuLwAXFqR6MNXJb5VQV8aOlUJykNnwleDthHLVxBkajyII5FjKQW9jXYqHQ0TUEP3hW4ZSot9LUxrV0swMwTznyfx4g33TM3HCJXdAXjbif9qza7P4OvhNLXxIYj8+EPHJzcww6ClUiDlcv4KGHztStncoxfjWodyUfh1W3Hq+z/LHOCx/26VZgsWA9QKqqRa/eLONE1frX+i0z76SEmSsvP8Dd2HVuQgY2l1Vwm6enUQW0F/pFWiaX0tPalxVrrg5OOWdM1XYN6LprfKYruimeiaJeqhXEN5E9ojY0tWdaotC87Qx2iB0sVVsoelLKEx/j542fRa0lu9x/BNKcQP1IuGuVotEP0l5jbeNhJCA8r0ldKv7ioUb+MvcQjSwiIhn9aXyrdY2fzWGC0EML2mk8ZWji5ZI9N4IFsrreJS3EJ8YAWXAw9VpW5Kk8jqoIVayZrBLb9ALvTZOrNEnt3mBMkCtL7Oe0rLpdMkDsMnik92R42uqQR8zpBy0LIm5Nq7CfAhVxuvWPKizN7oZcgTaVrOiyiL/xKQD407AzrQDOPpe0iJiAdKVBDcqIfey/JcxKuUagbKVI+hicuPcJ8pSE7ecIqhLVa5v7Ws4ssvypgmMA0noPPpKOuW/LFeGviNIyLdIKnN4qoAqy6Dzk9DXt1o1yTmm4/ymu1SKBV0NXtsLVrx0rdMN+mmWXogumINrVRGpnkv24ugBz62F4HNoS6ImW+eVMquFnDNlTnN8Q6KoItaCk6AawQQR3oeekzEFwtlOWWmmiz5ICdgRv5ORAk0bJ5uQqdXihXdAXq6zMe55VjffKOITAY+0IDZ1EviWJ0gatLiWx9s5b5bDYbPjrWZ425Lx2q1RNU13TFM3oxsGjnYqeiDqqKrKqtIZFPRUr/kLfWdmJzXNNUveVPl0HJbfWeYzWu80X5b31ZivfgjG+uXj2ms8vByj7b65R5tVSyO25VHQHYyXzHzCHTrtaOJ2cnAbGWExum22dDX4qjBAy2pwC+b0/KpGAR4uHG8KqwiK3Ss36h6dVO+AbaeC/PNHnkAVLU89lPMIP4JBss1bdxyH7JemdJ1J6af3YyVpb6YZT5SpJ6o2iaTioNYJTT9XQDzDSE76eqHa0JCk66K8dELMyi3HNjySZh5v+AUg+E2Oyyl4Aqx+fXyz8jMA8XRQ6LFW9Qlp72Jby8U+Yy29u5Nracvy6QMiPvaNXb51ACsBsJ9D3yHO07Z8/vpZESF0Fg/CF3m6Nzqb6td+8zq6/qnlHpawS7eZlO6zkZlV/l6oJ5gNB31q6zHqdXeWpGDbRU5r6pIEN2rIu3tFTEsZsokdp8Dlz88bOmlPKm80yO5tGNOKe4vctwPQ7jWoabDj7Qxr6FJSIFW4rgs2CwV9jd9uedTlmaayxbtZbiudumlaXBDTtgDLhtlTcy0i6KuAY0BjNKFtSxQ9WNEnvFnez7ddbXq7ULyRaOGza8seMGBRIoBLg8Xy6Ascaxez1S+Qm/3S/RK1+ipl+CVr80r5TMWMn23WPXFeOV7keiDEuzdBm788a4J0BLcEnEiPbQEmsOtrnYyNj9Y9IBdpJVHeNypt7SroIrTO7VgCxpxZG1uO53hgCwqYSWPNYjw9o62OnMurSuYj5qYgdE5noX0MLB9w1S6Erb+tJFBYpXnKHRIrFU+M7e7nYG9U3x3W/i6qs6rDAukjbRca0c29urOhuWHvJROUKsfMkiVcsgwFRuQ97RF55/QLJSuMcecb8PSXHxHja+qtSArO0c8Sa97V967XAIzHn6xMCiPXQCmmvlypkv3eYElEpUVNw1syOJtJf3azdslaBFcaQ/qeC5msa5bB6dsf5+SSL5Yp1y0ou+uCVCdLukGYaPrA7MAfxyVYPX9l2kFj/X8y1VCGH5VK6q2LOVtWb7Dn5/xveOPBWg+0Uk5iflxhuv0T0yJCL0Z722a1AvLrgPqAryO4+yH2CeNZuc6TjOYbc6itCMCUOJGRvg4Pj0EzzlYZuenh1lCSDoFE9BOVlH7miTk/BQ3uFt0h9XZ/iwOfXzN4yoCyvvnp1RO56d0YclCz+Fsf3pNpu+hDvtGnKssns9DRD2kSCp5etvHlTeZwCx4//ybMBvL2XYUZzTTPn/nrTtUCBYwDxAKGZyjRwWgAOGXxzcGrWYFVeuf//jfTsex/u//wf8v/vmP/6SP/P7zH/8Bvy+t30gSnwy2FCiyxS8uIbmWFJqsIcUn/r6ah2fEQQn9qykqFShGbT5k4s5Pnica9CpXRQ1Ze1lj//y1sO5MGTqdjuDbqAhUS3gzg+qHwZR2g8N4mpGsnQKOt9g/z5/LYl2J6iP7huo4zsMhM/pEQK26jhlYB3h4E8eReFKDk8iWPOCAI63ykEjDzshiia4K0iFJAgIDpxAPmxTuK3sR4d/e/PhDh06eG0iww8+E6/RY3e2mMk4ANUTJ3xSx1D7WMb2HwdgtHtPAcWJLBxR1Ec2oVGm/pl3epTHoz90+7ofcP7H2n7GQW36pDswYMFAIc2TLo4v/zY71HTqthywdt3GxlxBokHG/Ze1L7x8gxcfVP2c5OH1mAaF5D8QM7UkDSut/fvsE/lE86Q4FyLrb9xz45cJUZL84OgspF3f7eGoAsQECcmlvo18BynJOrAGm0iMDkAp9eR80Hj4NNpfw+VahhSnaiSORrN5CRDkSdSruK8KSwJZiOThq7C+8df6ZBzvhO5tl7edXQ+cg+Q3ReYp4Hhm5uBgdsV35Pfpuq9vrs699l349GkLOEf6jX4/h4xBfoGVfR8cA7OBTeex5Wwe/90bwn8tes3UxYYjvMQ5YQhcSuk4PEkbs3dg+ng/AEro9BuF08YQA/Y8VcoToI3y+z3FogovnCIYOTcWE4QBZxpKOKUYPP3Z7+XckNurn4FjiEVb6iL5M2wNqfaToupeXqAjKDnoQ0ZBpVVUgmmmLaDX++N6+wKEqg7q6j4GSWrALFYRe6kFVHZSmUMTiOS3UNpEglG9bGZfb2fj3yILRS4VjmZvLjTyg8MEDPoKpKJmnki3TbHCd4fq79Yxurzmxnr762XI4sW/m2dhcCmMADRb1ymAgY5PvEwsXi/atwEdnIbnKx7eJ4x6DBrn9vnfcH40mE//Yn04n3sDtH5HR0RFnb5sPkTsLjQung/evy/9At+irLGd8cGqa+L8v473e6Ghw7Mz8qd/r+wPX8wa9mTOYEedoCF23b2T8v4NvBb15UO9d9XvUver1hH/VHXEPC0YalCf1stzPbtZnN+uzmyVco8Gvz//2qvfuyY/vBvP4r/ODx88Oe6+mT359/Wx45P40P3x18/rZq8Hj37571v9z3Ht5OH/y24dXLx/Ht+96Lxdz5/DFD8ODVz/N3T8/O/4Q/+3nwwe6Zso/o5vmopvWspi/Nqj119yyv+bey1/b6vv1zLR6m0/n6bVh2BqM+iNnODxyjroOdXpyz8+Qtc0R7I56rtM/6h8PRo5zPByVHEMDzc9+4n8TP7Gn+om9rWW0rG2FuJ+d0U/kjLqO43tOl8yc2bA/9CZef9Tz/ZE7Gs6OZ8PuceHTIb1z9sYi+AgLDP1eKdyegFPU6Ci+28m1lzbOmRfTMXl1FOeEOoDEx5uqmtRAh7j++6VHf8DIQe1o6b+PY2nx4ndxMA1ZRlmYWhXAQ2+ZYib1Uw2KodH40uir/n/q09bKo1qhf2cXmM+gYAp13O/23L5r0aesjru94fHRAD93u0N3eMzTu86wOzjq0c8jmMb1+mBTRNgdiAy7YL4GFgUeHg36/cERQxx0B32nTwmOBmAXjno0XVhGhYgzOh66zqCPAIMBMNXrHrHS+/3+0HVp+ghIdx1OEI3nsXUJloIF85WpX4VgDePGR/vt7GaLFz69Vq9wNYnf6x33HL/rD0l/OBqOnAkMLZNe3xlOj0c+9c92nx7LwFunpDLw1rHucsynF/E0gRmFcYIh5gAoKw7EwTWvf7+Ti/MKs/dLqE/DmC4pi++dKSbQYugLGeMiJyEwik/JX4PsuqGg50RniTfn59W0dbpn/OtzDoF1ENDKWo8gjPmGo65542pHXdnbm+IsWk4ZvvIVwie3L4C4wMZL6Mfs1dZ4lUzJM3rJQ4UMv0RjtW8dWBo6v0JFFktOrTMLklSUTWsGCEVuMeHaqC1hFPK2+dX/A99dP7A=</script> 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -696,7 +675,7 @@
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} if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >eNrVWQtT4sgW/is9mbpLWIcICKj4qBuQlzPqKM7ouLvFNkknaUk6sdMBccv/fk8n4R2dmbuzu7VQJdA5fd7n9Hfaw1BMXXKsCU5IaPgBGXDfF+gPFPghFdRndcSJiwUdkwNk+UwULOxRd1pHns/8MMAGrE8cKkgh/lFHAYcVl4aiELMuiGkAq8xnsDzExsjmfsTMguG7Pq8nWw9Q+mvoAgHwo6Zw6siiAsiYIEwcII+yQrpeKhb/A7z8x0JInyizYZ/PTcILsHSAAmyasFhwiSXqqGw4UhtGCg6htgMrJa0q5TGBKRg3559+KYxpSIfUpQJMxJHw57QFygSnLKSGFEuSp6ldz4fbiR8P534s8IiBTA5rocFpIJB0xFEOB4FLDSxdu+0bgkg3cYK93LGq5o+OwfMgLxTIJBYL0RESDg01m4grCMu5bxI1rzl+KLT4OZhGBBoEhEmTdUNylZt++S3rSRcz0yXwmEWue5BI0EDNvu8zWFUnPh/l0bIO/g0syUcry4IacjEg3PK5h5lBNOZP1HycCCBA3XiCCsmmQ7RTzgMfaiF1TWvNJcwWDjo6QkVJ8qrqnIiIM/A7Im5IFoo5EZOarbMOHWoJqV9MIL88w/sFCSqkHzP9icbJQ0RCoTPqxeFqc+wRNfFJXvI42BAURKGTuPEgw8aZiKPEjFes/HYdpBZJIIVv225SvoO4xCBbA8lLrhBXvENkDAmeRlJqF//WRmQqna5wRSqUEmuGi8PwA1RxyldV5jwHHqShMhP+nAd/QvrHOX58uJ1VACYdo5jhkbLaZxQk8BAsJY9HSlGB0uVik8RnoCI4g8Gj14oh2wOq3DOzXYFiTPpd3HAGeDjkZBznT9x/3tb2yrhYBKtSAsP3PNi4RIHjlzR+jQTXmS/UuuOPCc9n0KfkoWwi9jLDnVK1VJUEYAXhnJiDANogcXwXetoy4a58p104NqOOqMDQTeRmC8jxEMxn4JcX+jfovEI2SDwG1CYNQeh01qfXCdExcvGQuPX6kEBlkyWtjPh1kCkvacGFkuzBae8G385lURY35qHry6b/oszYo5uSTcxHIcE2ZAvb3B0HY23JwaF6HPM8zvRDEkDDIcaImPk8+jm/0EFuzd40o1/RMD5L6ij3a7k6NHL/pHqrm15UsvY3KCnjKAVHPJQBDHw4UQnPkEvDHyc2LoVYUCHuAeFLOf5jpC7ME+RRbErRaDiwKA/FwGcDmf4ZpfVaKWnlqqymzFChP61+EvF1FaVVHuY2YKBEjbign/+kNOiHwXQYCQHgI6sBLR5nJa2ClDUqcCQg0GziX0mpYiprADZ3hiErKHZRf+oNfTdEF5GQ9pqomeyEz2AKhVGYkOEIwGjSeT04X5wYdmImYDvFITHnEPYtKcr3wWaaJ7tj6FjU9om3bmVSHxlWZLe7xU5tgsOBAecAOHa+H1ti5fSY9enXZK7tWRW57Ho0xlwtFEwscAEzCGwMTvLLy1KIRFwcs1k2x2xRKUQEPAZwuuBH4vtMmWsAgaHEfLOqSSwSvaFe4HOB2QbvIfdHhA3kyqIZfd27S9uW/DkL87Mm8QsoZg4MQL8mJyxVdXW+AJ6rhD+ouy6VTnqQrpSqgV1DhSEIEHgpeIxBmxYKLPfP9f3LNEnHskQT0xdgu9Ri2XkPEXYZYNoBjI0WfQQmK2WyF5cJAHgscdEEcwaFN5g19lksLAsbpZ0MwgAQ8B/ziZCnA6DsXqmT0qVCUYvb6mI+rcdDI+YFm2OTQthUVNqpmsR+h3xIaZugIqhXM5x3SYoDdJUNI15CqZs3dNnorD+mbaONBj2z51mbOJC6EuZm9Vh55EEjzqRJwNkSFUh9iVHCAaLh4gB64dfx5PcfFi9LWCiaEJFHaBrmizQ/Qo8sESuuWMxfWa5YHXC01cEKvcJh3VS2MuplE36LsBf5/MDbDTlwojc653iqWdz3YIg1Ijk4abLgQ22MXZh01XxeC30YceM2IEdV+aklR7YcU7/x0FZyUBT5+cVA6BAi5O0BmaBmv9+X1vTlmrwLiB/CpB1PXf0pM9Tf/5sCBYPMGtL3g4blMY3J2xA3XZukF1EVOWiG3KijiLuqPMHq8vn2xLes8sEQzsha5Z1Z3O+c2XpDj1+9S13342+Nqwn87bZ1vaW/9mp4um6P/Pdmr9VoTr7o+vWX5ql+1ms09bb92Ot+cETYOKPE3mmf3JY/9Gpfxv0goh/Pqtel09ve1eez8c3Zk/g4bbebWzf26Jo2TooOPbmMTltm577YHW5b454ZPLyvOQ83lF5GZ6zjdK1PQv9Ua5zzit7usVGrZnyKIrZ1VX0wwtFkbLXd7YdHu+Xv2cPTSWev1NW3mX5V/cD5aelqy34qXplF/dQq2ee7zUnnvmwX/Wl0tbvrtUq1Sfd2/8K2A3I9mlZIb/hUNYb8oiOwbl/2zicnOJyGl1Gvd3vTak/0j5dB74v5aXt7y9693r3dEUXr/ccHfVwFnh/08139bKJ79tNVfyu665PW7WPZqhlP55Wr7rQaNfT3T437oB3s0O5ls1W8iz5W+rvManxoddtnnk639satssNKzu7W8PPk9n7S5eOTzqcmu7daLVtsXRh3rrtb3W+eThp7zn7l7KzT3+nc6bbXq943LvfFdYd091uNRq+zc2JXrra/GNOh3oGYfn6/rV92sE7Omq7efWpd2HfCrjU+2hcXvZPGiF5WSbtx22y0DVoMHO4HDHIjuGudlJ5Ko77VtIQzfc+6Jm6HXat47nVa57WGqT98/hxgEfbvPNPEdL9sPe1XPtH7h1rg8dqF/6XZp7zjjU87O/2b/k67VTYal9b1Vtf1g06lHU6q2H6o7dE70j93gxvW6PaIecZJdPPQaXqlmzYf9fuP1XLt5iac6KBRHsU3gULNxWmdk0fj7/BnXv3Y9APABYuSjO8vNU17heJdUrO/Aa/Xb4Sc+EIthm4JqgTekB7MQGoC7lavO6EEr31ZvkCWgj+5FkJ7kCwkmpUQEE8wFYjhMbWx8LkGnIOhj7mpTTgV5BoGP3XBC4xNeS3u1ACQqMoS1JW3aSDlmnoEMLE6u27d2MeJB7h1Y+vzO1QuFosx7IDmCwhEjYe2bLlLeFZZKCfH1VkHkxeQCnqL2pi60NiEjyTxm7izAdBggNmgG1PwGcGmhONby75Lbwa/cicoof3sUnD13mcdJCnHh8kJfUhZEKUnjRKfyUP/Uclkkh7fsHU73rQqb/WEVY5/coVUFyhep1t5uIZclWPLxY8aY48SXwvi0qE245l89OWy+sfhdmrNErPcDFzlVpfl0sAdu6vLysbEobz2fO3h8jUkmP72sbx7YICNIvmaKltHL0fo3xKQVfc3pY1qpvcXw14O+awpC+so952dJb5nzufQfBI9Un7JxX7N/aagGAccKUuDah399BD54mBOlPw8QBtjNYCOuK6gbzvwPbV6zbpNN81Aamb400tr5RiqvITUCmrkX+b3F2dnjPyOEhJqHinza3BjWCrXSnv7Rrm4VxlWS/tmpWIUi5Wqae6Zxq6xxjfr+jxNpWxj7qFeY0CKsoyeT0Ux4YwkokzslNX8T/YiQ78egXffGaW/PCMTuP1NeZmQ/sns/IaPF3z/WvK+1CaW/7mjHAOWeJlowWsjoPnXW9S/OwH+jpb0Twa9hACJef9v6J9/ZOznlCYdH/8PIcW+UQ==</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_dbc06539d9c443b38e9c0572adf4f78f\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_d9ecfb6392a34945a6aadf520b5db673\" style=\"display:block\"></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_d9ecfb6392a34945a6aadf520b5db673\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_d9ecfb6392a34945a6aadf520b5db673\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -708,7 +687,7 @@
     {
      "data": {
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diff --git a/docs_nnx/nnx_basics.md b/docs_nnx/nnx_basics.md
index 149e859d2..c2aca4cae 100644
--- a/docs_nnx/nnx_basics.md
+++ b/docs_nnx/nnx_basics.md
@@ -40,7 +40,7 @@ class Linear(nnx.Module):
     self.din, self.dout = din, dout
 
   def __call__(self, x: jax.Array):
-    return x @ self.w + self.b
+    return x @ self.w + self.b[None]
 ```
 
 Also note that the inner values of `Variable`s can be accessed using the `value` property, but for convenience they implement all numeric operators and can be used directly in arithmetic expressions (as shown in the code above).
@@ -73,12 +73,12 @@ class Counter(nnx.Module):
     self.count = Count(jnp.array(0))
 
   def __call__(self):
-    self.count.value += 1
+    self.count[...] += 1
 
 counter = Counter()
-print(f'{counter.count.value = }')
+print(f'{counter.count[...] = }')
 counter()
-print(f'{counter.count.value = }')
+print(f'{counter.count[...] = }')
 ```
 
 Mutable references are usually avoided in JAX. But Flax NNX provides sound mechanisms
diff --git a/examples/gemma/helpers.py b/examples/gemma/helpers.py
index f74845bed..eeb02848d 100644
--- a/examples/gemma/helpers.py
+++ b/examples/gemma/helpers.py
@@ -74,7 +74,7 @@ def assign_val_fn(
         mapped_path: tuple[str | int, ...],
         val: Any,
     ) -> dict[tuple[str, ...], Any]:
-      state[mapped_path].value = val
+      state[mapped_path].set_value(val)
       return state
 
   mdl: M = nnx.eval_shape(module_factory)
diff --git a/examples/gemma/helpers_test.py b/examples/gemma/helpers_test.py
index 8d5e899f9..dd7e5fe4e 100644
--- a/examples/gemma/helpers_test.py
+++ b/examples/gemma/helpers_test.py
@@ -137,11 +137,11 @@ def _map_key_fn(key: tuple[str, ...]) -> tuple[str | int, ...]:
     np.testing.assert_array_equal(output, linen_output)
     for i in range(len(num_features)):
       np.testing.assert_array_equal(
-          mdl.layers[i].layers[0].mean.value,
+          mdl.layers[i].layers[0].mean[...],
           linen_vars['batch_stats'][f'layers_{i}']['layers_0']['mean'],
       )
       np.testing.assert_array_equal(
-          mdl.layers[i].layers[0].var.value,
+          mdl.layers[i].layers[0].var[...],
           linen_vars['batch_stats'][f'layers_{i}']['layers_0']['var'],
       )
 
diff --git a/examples/gemma/layers.py b/examples/gemma/layers.py
index f764c61a0..5fb959ada 100644
--- a/examples/gemma/layers.py
+++ b/examples/gemma/layers.py
@@ -44,11 +44,11 @@ def __init__(
     self.w = nnx.Param(kernel_init(rngs.params(), shape, dtype))
 
   def __call__(self, x: ArrayLike) -> Array:
-    return jnp.einsum(self.einsum_str, x, self.w.value)
+    return jnp.einsum(self.einsum_str, x, self.w[...])
 
   @property
   def shape(self) -> Shape:
-    return self.w.value.shape
+    return self.w.shape
 
 
 class RMSNorm(nnx.Module):
@@ -65,12 +65,12 @@ def __init__(
     self.scale = nnx.Param(scale_init(rngs.params(), dim, dtype))
 
   def __call__(self, x: Array) -> Array:
-    dtype = self.scale.value.dtype
+    dtype = self.scale.dtype
     var = jnp.mean(jnp.square(x), axis=-1, keepdims=True)
     normed_inputs = jnp.asarray(x * jax.lax.rsqrt(var + 1e-06), dtype=dtype)
     # normed_inputs is a rank-K tensor, K > 1 (K is typically 2 or 3). scale is
     # a rank-1 tensor. To avoid implicit rank-promotion, reshape scale to
     # a (1, ..., 1, D) tensor, so the rank of scale matches normed_inputs.
-    scale = jnp.expand_dims(self.scale.value, axis=range(len(x.shape) - 1))
+    scale = jnp.expand_dims(self.scale, axis=range(len(x.shape) - 1))
     normed_inputs = normed_inputs * (1 + scale)
     return normed_inputs
diff --git a/examples/gemma/modules.py b/examples/gemma/modules.py
index 56c426fbd..48c9a018a 100644
--- a/examples/gemma/modules.py
+++ b/examples/gemma/modules.py
@@ -63,15 +63,15 @@ def encode(self, x: ArrayLike) -> Array:
     return x
 
   def decode(self, x: ArrayLike) -> Array:
-    return jnp.dot(x, self.input_embedding.value.T)
+    return jnp.dot(x, self.input_embedding.T)
 
   @property
   def embed_dim(self):
-    return self.input_embedding.value.shape[1]
+    return self.input_embedding.shape[1]
 
   @property
   def num_embed(self):
-    return self.input_embedding.value.shape[0]
+    return self.input_embedding.shape[0]
 
 
 class Attention(nnx.Module):
diff --git a/examples/gemma/sampler_test.py b/examples/gemma/sampler_test.py
index 307b0e43e..8d2ed5a83 100644
--- a/examples/gemma/sampler_test.py
+++ b/examples/gemma/sampler_test.py
@@ -232,9 +232,9 @@ def test_forbidden_tokens(self):
         transformer_config, rngs=nnx.Rngs(params=0)
     )
     # Pre-cook the embedding matrix so that the output is deterministic.
-    transformer.embedder.input_embedding.value = jnp.eye(
+    transformer.embedder.input_embedding.set_value(jnp.eye(
         vocab.GetPieceSize(), 32
-    )
+    ))
     sampler = sampler_lib.Sampler(
         transformer=transformer,
         vocab=vocab,
diff --git a/examples/gemma/sow_lib.py b/examples/gemma/sow_lib.py
index 6bc808501..7580cdfe2 100644
--- a/examples/gemma/sow_lib.py
+++ b/examples/gemma/sow_lib.py
@@ -49,13 +49,11 @@ def merge(self, decoding_step, layer: nnx.Module):
         if field.name.startswith('attn_'):
           step_value = getattr(
               layer.attn, field.name.replace('attn_', '')
-          ).value[0]
+          )[0]
         elif field.name.startswith('mlp_'):
-          step_value = getattr(layer.mlp, field.name.replace('mlp_', '')).value[
-              0
-          ]
+          step_value = getattr(layer.mlp, field.name.replace('mlp_', ''))[0]
         else:
-          step_value = getattr(layer, field.name).value[0]
+          step_value = getattr(layer, field.name)[0]
       except AttributeError as exc:
         raise ValueError(
             f'Intermediate {field.name} is not in the step intermediates.'
@@ -93,7 +91,7 @@ def merge(self, decoding_step, transformer: nnx.Module):
     if self.embeddings is not None:
       try:
         self.embeddings = self.embeddings.at[:, decoding_step + 1, ...].set(
-            transformer.embeddings.value[0][:, 0, ...]
+            transformer.embeddings[0][:, 0, ...]
         )
       except AttributeError as exc:
         raise ValueError(
diff --git a/examples/gemma/transformer.py b/examples/gemma/transformer.py
index 842121e96..54fb6748e 100644
--- a/examples/gemma/transformer.py
+++ b/examples/gemma/transformer.py
@@ -487,10 +487,10 @@ def _assign_linen_params_to_nnx_state(
   if 'gate_proj' in mapped_path:
     if transpose_gating_einsum:
       val = jnp.swapaxes(val, 1, 2)
-    state[mapped_path].value = val[0]
-    state[mapped_path[:-2] + ('up_proj', 'kernel')].value = val[1]
+    state[mapped_path].set_value(val[0])
+    state[mapped_path[:-2] + ('up_proj', 'kernel')].set_value(val[1])
   else:
-    state[mapped_path].value = val
+    state[mapped_path].set_value(val)
   return state
 
 
diff --git a/examples/gemma/transformer_test.py b/examples/gemma/transformer_test.py
index 3d30f9277..97916604b 100644
--- a/examples/gemma/transformer_test.py
+++ b/examples/gemma/transformer_test.py
@@ -461,7 +461,7 @@ def test_sow_intermediates(self, sow_config):
 
     if sow_config.embeddings:
       self.assertTrue(hasattr(transformer, 'embeddings'))
-      embeddings = transformer.embeddings.value[0]
+      embeddings = transformer.embeddings[0]
       self.assertEqual(
           embeddings.shape,
           (batch_size, sequence_length, config.embed_dim),
@@ -472,7 +472,7 @@ def test_sow_intermediates(self, sow_config):
     for layer in transformer.layers:
       if sow_config.rs_after_attention:
         self.assertTrue(hasattr(layer, 'rs_after_attention'))
-        rs_after_attention = layer.rs_after_attention.value[0]
+        rs_after_attention = layer.rs_after_attention[0]
         self.assertIsNotNone(rs_after_attention)
         self.assertEqual(
             rs_after_attention.shape,
@@ -482,7 +482,7 @@ def test_sow_intermediates(self, sow_config):
         self.assertFalse(hasattr(layer, 'rs_after_attention'))
       if sow_config.rs_after_ffw:
         self.assertTrue(hasattr(layer, 'rs_after_ffw'))
-        rs_after_ffw = layer.rs_after_ffw.value[0]
+        rs_after_ffw = layer.rs_after_ffw[0]
         self.assertIsNotNone(rs_after_ffw)
         self.assertEqual(
             rs_after_ffw.shape,
@@ -492,7 +492,7 @@ def test_sow_intermediates(self, sow_config):
         self.assertFalse(hasattr(layer, 'rs_after_ffw'))
       if sow_config.attn_logits_topk:
         self.assertTrue(hasattr(layer.attn, 'logits_topk_values'))
-        attn_logits_topk_values = layer.attn.logits_topk_values.value[0]
+        attn_logits_topk_values = layer.attn.logits_topk_values[0]
         self.assertIsNotNone(attn_logits_topk_values)
         self.assertEqual(
             attn_logits_topk_values.shape,
@@ -504,7 +504,7 @@ def test_sow_intermediates(self, sow_config):
             ),
         )
         self.assertTrue(hasattr(layer.attn, 'logits_topk_indices'))
-        attn_logits_topk_indices = layer.attn.logits_topk_indices.value[0]
+        attn_logits_topk_indices = layer.attn.logits_topk_indices[0]
         self.assertIsNotNone(attn_logits_topk_indices)
         self.assertEqual(
             attn_logits_topk_indices.shape,
@@ -520,7 +520,7 @@ def test_sow_intermediates(self, sow_config):
         self.assertFalse(hasattr(layer.attn, 'logits_topk_indices'))
       if sow_config.mlp_hidden_topk:
         self.assertTrue(hasattr(layer.mlp, 'hidden_topk_values'))
-        ffw_hidden_topk_values = layer.mlp.hidden_topk_values.value[0]
+        ffw_hidden_topk_values = layer.mlp.hidden_topk_values[0]
         self.assertIsNotNone(ffw_hidden_topk_values)
         self.assertEqual(
             ffw_hidden_topk_values.shape,
@@ -531,7 +531,7 @@ def test_sow_intermediates(self, sow_config):
             ),
         )
         self.assertTrue(hasattr(layer.mlp, 'hidden_topk_indices'))
-        ffw_hidden_topk_indices = layer.mlp.hidden_topk_indices.value[0]
+        ffw_hidden_topk_indices = layer.mlp.hidden_topk_indices[0]
         self.assertIsNotNone(ffw_hidden_topk_indices)
         self.assertEqual(
             ffw_hidden_topk_indices.shape,
diff --git a/examples/nnx_toy_examples/mutable_array_basic.py b/examples/nnx_toy_examples/hijax_basic.py
similarity index 91%
rename from examples/nnx_toy_examples/mutable_array_basic.py
rename to examples/nnx_toy_examples/hijax_basic.py
index 7386163c1..294508871 100644
--- a/examples/nnx_toy_examples/mutable_array_basic.py
+++ b/examples/nnx_toy_examples/hijax_basic.py
@@ -54,9 +54,9 @@ def __call__(self, x):
     self.count[...] += 1
     return self.linear2(jax.nn.relu(self.linear1(x)) * 0.5)
 
-with nnx.use_refs(True):
-  model = MLP(din=1, dhidden=32, dout=1, rngs=nnx.Rngs(0))
-  optimizer = nnx.Optimizer(model, optax.sgd(learning_rate=0.1), wrt=nnx.Param)
+nnx.use_hijax(True)
+model = MLP(din=1, dhidden=32, dout=1, rngs=nnx.Rngs(0))
+optimizer = nnx.Optimizer(model, optax.sgd(learning_rate=0.1), wrt=nnx.Param)
 
 
 @jax.jit
@@ -67,7 +67,7 @@ def loss_fn(params):
     model = nnx.merge(graphdef, params, counts)
     return jnp.mean((y - model(x)) ** 2)
 
-  grads = jax.grad(loss_fn)(nnx.to_arrays(params))
+  grads = jax.grad(loss_fn)(nnx.to_lojax(params))
   optimizer.update(model, grads)
 
 
diff --git a/examples/nnx_toy_examples/mutable_array_demo.py b/examples/nnx_toy_examples/hijax_demo.py
similarity index 81%
rename from examples/nnx_toy_examples/mutable_array_demo.py
rename to examples/nnx_toy_examples/hijax_demo.py
index 6d9619444..9b14e3404 100644
--- a/examples/nnx_toy_examples/mutable_array_demo.py
+++ b/examples/nnx_toy_examples/hijax_demo.py
@@ -49,9 +49,8 @@ def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
     self.w = nnx.Param(initializer(rngs.params(), (din, dout)))
     self.b = nnx.Param(jnp.zeros((dout,)))
 
-  # [...] is used to access the array
   def __call__(self, x: jax.Array):
-    return x @ self.w[...] + self.b[None]
+    return x @ self.w + self.b[None]
 
 
 # Block implements linear, batch norm, and dropout. Its behavior
@@ -88,21 +87,21 @@ def __call__(
     self, x: jax.Array, *, rngs: nnx.Rngs | None = None
   ) -> jax.Array:
     # ----------- linear --------------------
-    x = x @ self.w[...] + self.b[None]
+    x = x @ self.w + self.b[None]
     # ----------- batch norm ----------------
     if self.use_stats:
-      mean = self.mean[...]
-      var = self.var[...]
+      mean = self.mean
+      var = self.var
     else:
       mean = jnp.mean(x, axis=0)
       var = jnp.var(x, axis=0)
       # ema updates
-      # stop gradient is used until a ArrayRef supports updates from grad tracers
+      # stop gradient is used until a Hijax supports updates from grad tracers
       sg = jax.lax.stop_gradient
-      self.mean[...] = sg(self.mu * self.mean[...] + (1 - self.mu) * mean)
-      self.var[...] = sg(self.mu * self.var[...] + (1 - self.mu) * var)
+      self.mean[...] = sg(self.mu * self.mean + (1 - self.mu) * mean)
+      self.var[...] = sg(self.mu * self.var + (1 - self.mu) * var)
     x = (x - mean[None]) / jnp.sqrt(var[None] + 1e-5)
-    x = x * self.scale[...] + self.bias[...]
+    x = x * self.scale + self.bias
     # ----------- dropout -------------------
     if not self.deterministic and self.dropout_rate > 0.0:
       assert rngs is not None
@@ -125,7 +124,7 @@ def __init__(
     use_scan: bool = True,
     rngs: nnx.Rngs,
   ):
-    self.count: jax.Ref = jax.new_ref(jnp.array(0))
+    self.count = nnx.Variable(jnp.array(0))
     self.block_in = Block(din, dhidden, rngs=rngs)
     self.linear_out = Linear(dhidden, dout, rngs=rngs)
 
@@ -136,11 +135,15 @@ def __init__(
 
       @jax.vmap
       def create_block(rngs, /):
-        return nnx.to_arrays(Block(dhidden, dhidden, rngs=rngs))
+        # return nnx.stateless(Block(dhidden, dhidden, rngs=rngs))
+        return Block(dhidden, dhidden, rngs=rngs)
 
-      self.blocks = nnx.to_refs(create_block(rngs.fork(split=num_blocks)))
+      # self.blocks = nnx.stateful(create_block(rngs.fork(split=num_blocks)))
+      self.blocks = create_block(rngs.fork(split=num_blocks))
     else:
-      self.blocks = nnx.List([Block(dhidden, dhidden, rngs=rngs) for i in range(num_blocks)])
+      self.blocks = nnx.List(
+        [Block(dhidden, dhidden, rngs=rngs) for i in range(num_blocks)]
+      )
 
   def __call__(self, x: jax.Array, *, rngs: nnx.Rngs | None = None):
     self.count[...] += 1
@@ -169,7 +172,7 @@ class OptState(nnx.Variable): ...
 
 
 # Optimizer are an interesting case as they are inherently stateful and
-# pose a good use case for ArrayRef. Here we implement SGD with
+# pose a good use case for MutableHijax. Here we implement SGD with
 # momentum. The optimizer receives the params as constructor arguments but doesn't
 # hold a reference to them, it only uses the params to initialize its state
 # by creating new OptState Variables that reuse the param's metadata.
@@ -180,40 +183,36 @@ def __init__(self, params, lr: float, decay: float = 0.9):
 
     def make_opt_state(x):
       if isinstance(x, nnx.Variable):
-        return OptState(jnp.zeros_like(x.value), **x.get_metadata())
+        return OptState(jnp.zeros_like(x[...]), **x.get_metadata())
       else:
         return OptState(jnp.zeros_like(x))
 
-    self.momentum = nnx.data(jax.tree.map(
-        make_opt_state,
-        params,
-        is_leaf=lambda x: isinstance(x, nnx.Variable),
-      ))
+    self.momentum = nnx.data(jax.tree.map(make_opt_state, params))
 
   # during the update we simply map over (params, momentum, grads),
   # for each triplet we implement the SGD update rule which updates
   # both the optimizer's state (momentum) and the params in place.
   def update(self, params, grads):
-    params = nnx.pure(params)
-    grads = nnx.pure(grads)
-    momentum = nnx.pure(self.momentum)
-
     def update_fn(
-      param: jax.Ref, momentum: jax.Ref, grad: jax.Array
+      param: nnx.Variable[jax.Array],
+      momentum: nnx.Variable[jax.Array],
+      grad: nnx.Variable[jax.Array],
     ):
-      momentum[...] = self.decay * momentum[...] + (1 - self.decay) * grad[...]
-      param[...] -= self.lr * momentum[...]
+      momentum[...] = self.decay * momentum + (1 - self.decay) * grad
+      param[...] -= self.lr * momentum
+
+    # is_leaf might not be necesarry as MutableHijaxVariable are not pytreees
+    jax.tree.map(update_fn, params, self.momentum, grads)
 
-    jax.tree.map(update_fn, params, momentum, grads)
 
 # ## Training
 
-with nnx.use_refs(True):
-  rngs = nnx.Rngs(params=0, dropout=1)
-  model = Model(
-    num_blocks=3, din=1, dhidden=256, dout=1, use_scan=False, rngs=rngs
-  )
-  optimizer = SGD(params=nnx.state(model, nnx.Param), lr=3e-3, decay=0.99)
+nnx.use_hijax(True)
+rngs = nnx.Rngs(params=0, dropout=1)
+model = Model(
+  num_blocks=3, din=1, dhidden=256, dout=1, use_scan=False, rngs=rngs
+)
+optimizer = SGD(params=nnx.state(model, nnx.Param), lr=3e-3, decay=0.99)
 
 # Create a copy of the model structure and set its attributes to eval model.
 # This works because they share the underlying ArrayRefs so both models
@@ -237,13 +236,14 @@ def loss_fn(params):
     loss = jnp.mean((model(x, rngs=rngs) - y) ** 2)
     return loss
 
-  # For the time being we have to use 'freeze' make the Variables immutable
-  # as 'jax.grad' doesn't support ArrayRefs yet.
-  grads = jax.grad(loss_fn)(nnx.to_arrays(params))
+  # For the time being we have to use 'to_lojax'
+  # as 'jax.grad' doesn't support Hijax types yet.
+  grads = jax.grad(loss_fn)(nnx.to_lojax(params))
   # 'update' mutates the optimizer's state and the params in place
   # so we don't need to return anything 🚀
   optimizer.update(params, grads)
 
+
 # simple test step that computes the loss
 @jax.jit
 def test_step(model: Model, x, y):
diff --git a/flax/configurations.py b/flax/configurations.py
index 9240b697f..ca34a387f 100644
--- a/flax/configurations.py
+++ b/flax/configurations.py
@@ -27,7 +27,7 @@ class Config:
   flax_pytree_module: bool
   flax_max_repr_depth: int | None
   flax_always_shard_variable: bool
-  flax_hijax_variable: bool
+  flax_variable_mode: str
   # See https://google.github.io/pytype/faq.html.
   _HAS_DYNAMIC_ATTRIBUTES = True
 
@@ -201,6 +201,38 @@ def static_bool_env(varname: str, default: bool) -> bool:
     )
 
 
+def str_flag(name: str, *, default: str, help: str) -> FlagHolder[str]:
+  """Set up a string flag.
+
+  Example::
+
+    some_string = str_flag(
+        name='flax_some_string',
+        default='default_value',
+        help='Some string configuration.',
+    )
+
+  Now the ``FLAX_SOME_STRING`` shell environment variable can be used to
+  control the process-level value of the flag, in addition to using e.g.
+  ``config.update("flax_some_string", "new_value")`` directly.
+
+  Args:
+    name: converted to lowercase to define the name of the flag. It is
+      converted to uppercase to define the corresponding shell environment
+      variable.
+    default: a default value for the flag.
+    help: used to populate the docstring of the returned flag holder object.
+
+  Returns:
+    A flag holder object for accessing the value of the flag.
+  """
+  name = name.lower()
+  config._add_option(name, static_str_env(name.upper(), default))
+  fh = FlagHolder[str](name, help)
+  setattr(Config, name, property(lambda _: fh.value, doc=help))
+  return fh
+
+
 def static_int_env(varname: str, default: int | None) -> int | None:
   """Read an environment variable and interpret it as an integer.
 
@@ -222,6 +254,18 @@ def static_int_env(varname: str, default: int | None) -> int | None:
     ) from None
 
 
+def static_str_env(varname: str, default: str) -> str:
+  """Read an environment variable and interpret it as a string.
+
+  Args:
+    varname: the name of the variable
+    default: the default string value
+  Returns:
+    string return value derived from defaults and environment.
+  """
+  return os.getenv(varname, default)
+
+
 # Flax Global Configuration Variables:
 
 flax_filter_frames = bool_flag(
@@ -291,8 +335,8 @@ def static_int_env(varname: str, default: int | None) -> int | None:
   default=True,
   help='Whether a `nnx.Variable` should always automatically be sharded if it contains sharding annotations.',
 )
-flax_hijax_variable = bool_flag(
-  name='flax_hijax_variable',
-  default=False,
-  help='Whether to enable HiJAX support for `nnx.Variable`.',
+flax_variable_mode = str_flag(
+  name='flax_variable_mode',
+  default='lojax',
+  help='The variable mode for `nnx.Variable`. Options are "lojax", "hijax", and "ref".',
 )
\ No newline at end of file
diff --git a/flax/nnx/__init__.py b/flax/nnx/__init__.py
index 3ef14ce4c..066f3e99d 100644
--- a/flax/nnx/__init__.py
+++ b/flax/nnx/__init__.py
@@ -68,6 +68,8 @@
 from .graph import variables as variables
 from .graph import to_arrays as to_arrays
 from .graph import to_refs as to_refs
+from .graph import to_hijax as to_hijax
+from .graph import to_lojax as to_lojax
 from .graph import pure as pure
 from .graph import cached_partial as cached_partial
 from .graph import flatten as flatten
@@ -192,8 +194,8 @@
 from .variablelib import variable_type_from_name as variable_type_from_name
 from .variablelib import variable_name_from_type as variable_name_from_type
 from .variablelib import register_variable_name as register_variable_name
-from .variablelib import use_refs as use_refs
-from .variablelib import using_refs as using_refs
+from .variablelib import variable_mode as variable_mode
+from .variablelib import current_variable_mode as current_variable_mode
 from .visualization import display as display
 from .extract import to_tree as to_tree
 from .extract import from_tree as from_tree
diff --git a/flax/nnx/bridge/module.py b/flax/nnx/bridge/module.py
index ed30d2895..bf913e386 100644
--- a/flax/nnx/bridge/module.py
+++ b/flax/nnx/bridge/module.py
@@ -391,7 +391,7 @@ def _get_variables(self) -> tp.Mapping:
       if isinstance(
         variable, variablelib.Variable
       ) and bridge_variables.is_vanilla_variable(variable):
-        leaf = variable.value
+        leaf = variable.get_value()
       else:
         leaf = bridge_variables.to_linen_var(variable)
 
diff --git a/flax/nnx/bridge/variables.py b/flax/nnx/bridge/variables.py
index e354f2e70..b0584d388 100644
--- a/flax/nnx/bridge/variables.py
+++ b/flax/nnx/bridge/variables.py
@@ -64,7 +64,7 @@ def remove_axis(self, index: int, params: dict[Any, Any]) -> 'NNXMeta[A]':
   def get_partition_spec(self) -> jax.sharding.PartitionSpec:
     """Returns the ``Partitionspec`` for this partitioned value."""
     nnx_var = self.to_nnx_variable()
-    spec = spmd.get_partition_spec(nnx_var).raw_value
+    spec = spmd.get_partition_spec(nnx_var).get_raw_value()
     assert isinstance(spec, jax.sharding.PartitionSpec)
     return spec
 
@@ -78,11 +78,11 @@ def is_vanilla_variable(vs: variablelib.Variable) -> bool:
   Returns False only if it has non-empty hooks or any non-built-in attribute.
   """
   for key, value in vs.get_metadata().items():
-    if key.endswith('_hooks'):
-      if value != ():
-        return False
-    else:
-      return False
+    if key in ('is_hijax', 'eager_sharding'):
+      continue
+    if key.endswith('_hooks') and value == ():
+      continue
+    return False
   return True
 
 
@@ -91,11 +91,11 @@ def to_linen_var(vs: variablelib.Variable) -> meta.AxisMetadata:
   if 'linen_meta_type' in metadata:
     linen_type = metadata['linen_meta_type']
     if hasattr(linen_type, 'from_nnx_metadata'):
-      return linen_type.from_nnx_metadata({'value': vs.value, **metadata})
-    return linen_type(vs.value, **metadata)
+      return linen_type.from_nnx_metadata({'value': vs.get_value(), **metadata})
+    return linen_type(vs.get_value(), **metadata)
   if is_vanilla_variable(vs):
-    return vs.value
-  return NNXMeta(type(vs), vs.value, metadata)
+    return vs.get_value()
+  return NNXMeta(type(vs), vs.get_value(), metadata)
 
 
 def get_col_name(keypath: tp.Sequence[Any]) -> str:
diff --git a/flax/nnx/bridge/wrappers.py b/flax/nnx/bridge/wrappers.py
index 6ab594e81..50a1041e5 100644
--- a/flax/nnx/bridge/wrappers.py
+++ b/flax/nnx/bridge/wrappers.py
@@ -413,7 +413,7 @@ def _to_linen_var(x):
             if self.metadata_fn is not None:
               return self.metadata_fn(x)  # pylint: disable=too-many-function-args
             else:
-              return x.value
+              return x.get_value()
           return x
 
         collection_state = nnx.traversals.unflatten_mapping(flat_state)
diff --git a/flax/nnx/extract.py b/flax/nnx/extract.py
index fd8db8124..fe48ecc3d 100644
--- a/flax/nnx/extract.py
+++ b/flax/nnx/extract.py
@@ -63,7 +63,7 @@ def check_consistent_aliasing(
           lambda: f'Trying to extract graph node from different trace level, got {value!r}'
         )
       if isinstance(value, graph.Variable):
-        if not value._trace_state.is_valid():
+        if not value.trace_state.is_valid():
           raise ValueError(
             f'Cannot extract graph node from different trace level, got {value!r}'
           )
diff --git a/flax/nnx/graph.py b/flax/nnx/graph.py
index 45c0dede5..db98ceedd 100644
--- a/flax/nnx/graph.py
+++ b/flax/nnx/graph.py
@@ -20,7 +20,7 @@
 import threading
 import typing as tp
 
-import jax.experimental
+import jax.core
 
 from flax import config
 from flax.nnx import filterlib, reprlib, traversals, variablelib
@@ -34,6 +34,7 @@
 from flax.nnx.variablelib import Variable, is_array_ref
 from flax.typing import Key, PathParts, is_key_like
 import jax
+from jax._src import hijax
 import numpy as np
 import treescope  # type: ignore[import-not-found,import-untyped]
 import typing_extensions as tpe
@@ -373,7 +374,6 @@ class VariableDef(reprlib.Representable, tp.Generic[Node]):
   index: int
   outer_index: int | None
   metadata: HashableMapping[str, tp.Any]
-  array_refdef: ArrayRefDef | NodeRef | None
 
   def with_no_outer_index(self) -> VariableDef:
     return VariableDef(
@@ -381,9 +381,6 @@ def with_no_outer_index(self) -> VariableDef:
       index=self.index,
       outer_index=None,
       metadata=self.metadata,
-      array_refdef=self.array_refdef.with_no_outer_index()
-      if isinstance(self.array_refdef, ArrayRefDef)
-      else self.array_refdef,
     )
 
   def with_same_outer_index(self) -> VariableDef:
@@ -392,9 +389,6 @@ def with_same_outer_index(self) -> VariableDef:
       index=self.index,
       outer_index=self.index,
       metadata=self.metadata,
-      array_refdef=self.array_refdef.with_same_outer_index()
-      if isinstance(self.array_refdef, ArrayRefDef)
-      else self.array_refdef,
     )
 
   def __nnx_repr__(self):
@@ -761,32 +755,21 @@ def make_mutable_arraydef(value: variablelib.Ref):
   if is_variable:
     assert isinstance(node, Variable)
     assert index is not None
-    prev_inner_value = node.raw_value
-    if variablelib.is_array_ref(prev_inner_value):
-      array_refdef, inner_value = make_mutable_arraydef(prev_inner_value)
-    else:
-      array_refdef = None
-      inner_value = prev_inner_value
     if path is None:
-      leaf = inner_value
+      leaf = node.get_raw_value()
     else:
       leaf = node  # type: ignore[assignment]
-      if inner_value is not prev_inner_value:
-        leaf.raw_value = inner_value
 
     variabledef = VariableDef(
-      type=type(node),
+      type=type(node),  # type: ignore
       index=index,
       outer_index=ref_outer_index.get(node, None) if ref_outer_index else None,
-      metadata=HashableMapping(node._var_metadata),
-      array_refdef=array_refdef,
+      metadata=HashableMapping(node.get_metadata()),
     )
-    if type(inner_value) is not Repeated:
-      assert not isinstance(leaf, Repeated)
-      leaves.append(leaf)
-      if path is not None:
-        assert paths is not None
-        paths.append(tuple(path))
+    leaves.append(leaf)
+    if path is not None:
+      assert paths is not None
+      paths.append(tuple(path))
     nodes.append(variabledef)
     return
   elif is_array_ref:
@@ -944,7 +927,7 @@ def _graph_fingerprint(
         variable_index = new_ref_index[value] = ctx.next_index
         ctx.next_index += 1
         append_fn(variable_index)
-        for key_value in value._var_metadata.items():
+        for key_value in value.get_metadata().items():
           append_fn(key_value)
     elif not isinstance(value, (jax.Array, np.ndarray)):
       append_fn(value)
@@ -1048,7 +1031,7 @@ def _check_graph_fingerprint(
         # append_fn(variable_index)
         if variable_index != next(fp_iterator):
           return False
-        for key_value in value._var_metadata.items():
+        for key_value in value.get_metadata().items():
           # append_fn(key_value)
           if key_value != next(fp_iterator):
             return False
@@ -1197,10 +1180,10 @@ def get_mutable_array(array_refdef: ArrayRefDef, leaf):
         raise RuntimeError(f'Expected a no update for ArrayRef but got {leaf}.')
     elif type(leaf) in (NoUpdate, Repeated):
       raise ValueError(
-        f"Expected a ArrayRefOutput type but got '{leaf.value}.'"
+        f"Expected a ArrayRefOutput type but got '{leaf}.'"
       )
     elif type(leaf) is ArrayRefOutput:
-      array_ref = variablelib.new_ref(leaf.value)
+      array_ref = jax.new_ref(leaf.value)
     elif variablelib.is_array_ref(leaf):
       array_ref = leaf
     else:
@@ -1217,26 +1200,9 @@ def get_mutable_array(array_refdef: ArrayRefDef, leaf):
     variabledef = tp.cast(VariableDef[Variable], nodedef)
     # its a unseen variable, create a new one
 
-    if variabledef.array_refdef is not None:
-      if type(variabledef.array_refdef) is NodeRef:
-        value = index_ref[variabledef.array_refdef.index]
-      else:
-        value = next(leaves_iter)
-        assert type(variabledef.array_refdef) is ArrayRefDef
-        if isinstance(value, Variable):
-          value = value.copy() if copy_variables else value
-          inner_value = value.raw_value
-          array_ref = get_mutable_array(variabledef.array_refdef, inner_value)
-          if array_ref is not inner_value:
-            value.raw_value = array_ref
-        else:
-          # if value is an array or array ref, we need call get_mutable_array
-          # to register it in the index_ref
-          value = get_mutable_array(variabledef.array_refdef, value)
-    else:
-      value = next(leaves_iter)
-      if isinstance(value, Variable) and copy_variables:
-        value = value.copy()
+    value = next(leaves_iter)
+    if isinstance(value, Variable) and copy_variables:
+      value = value.copy()
 
     # when idxmap is present, check if the Varable exists there
     # and update existing variables if it does
@@ -1252,7 +1218,7 @@ def get_mutable_array(array_refdef: ArrayRefDef, leaf):
       elif isinstance(value, Variable):
         variable.update_from_state(value)
       else:
-        variable.raw_value = value
+        variable.set_value(value)
     else:  # variabledef.index not in index_ref_cache
       # variable reference does not exist outside, create a new one
       if isinstance(value, Variable):
@@ -1437,12 +1403,12 @@ def _update_variable(node: Variable, value):
         # can happen when using standalone Variables with `grad`
         pass
       else:
-        if is_array_ref(node.raw_value) and (
+        if is_array_ref(node.get_value()) and (
           isinstance(value, jax.Array) or is_array_ref(value)
         ):
           node[...] = value[...]
         else:
-          node.raw_value = value
+          node.set_raw_value(value)
 
   if isinstance(node, Variable):
     _update_variable(node, state)
@@ -1780,7 +1746,7 @@ def flatten(  # type: ignore[invalid-annotation]
       else:
         paths = None
         leaves = [
-          variable.raw_value for variable in node_static_cache.variables
+          variable.get_value() for variable in node_static_cache.variables
         ]
     else:
       graphdef, flat_state = flatten(
@@ -1916,7 +1882,7 @@ def unflatten(  # type: ignore[invalid-annotation]
             if isinstance(leaf, Variable):
               variable.update_from_state(leaf)
             else:
-              variable.raw_value = leaf
+              variable.set_value(leaf)
           self.index_ref.update(static_cache_node.new_index_ref)
         else:
           # uncached node, create it
@@ -2556,7 +2522,7 @@ def pop(
     >>> assert hasattr(model, 'i')
 
     >>> intermediates = nnx.pop(model, nnx.Intermediate)
-    >>> assert intermediates['i'].value[0].shape == (1, 3)
+    >>> assert intermediates['i'][0].shape == (1, 3)
     >>> assert not hasattr(model, 'i')
 
   Args:
@@ -2616,7 +2582,7 @@ def clone(node: Node, variables: bool = True) -> Node:
 
 
 def _mutable_like(path, x):
-  return (isinstance(x, Variable) and x.has_ref) or variablelib.is_array_ref(x)
+  return variablelib.is_array_ref(x)
 
 
 def to_arrays(
@@ -2687,7 +2653,7 @@ def to_arrays(
 
 
 def _array_like(path, x):
-  return (isinstance(x, Variable) and not x.has_ref) or isinstance(x, jax.Array)
+  return isinstance(x, jax.Array)
 
 
 def to_refs(node: A, /, only: filterlib.Filter = _array_like) -> A:
@@ -2743,11 +2709,83 @@ def to_refs(node: A, /, only: filterlib.Filter = _array_like) -> A:
     raise ValueError(f'Found duplicate at paths:{duplicates_strs}')
 
   graphdef, frozen_state, rest = split(node, only, ...)  # type: ignore[misc]
-  mutable_state = jax.tree.map(variablelib.new_ref, frozen_state)
+  mutable_state = jax.tree.map(jax.new_ref, frozen_state)
   node = merge(graphdef, mutable_state, rest)
   return node
 
 
+def _is_lojax_variable(path, x):
+  return isinstance(x, variablelib.Variable) and not x.mode == 'hijax'
+
+
+def to_hijax(
+  node: A, /, *, only: filterlib.Filter = ..., mutable: bool = True
+) -> A:
+  """ """
+  if not mutable:
+    raise ValueError('to_hijax only supports mutable=True at the moment.')
+
+  only = filterlib.All(_is_lojax_variable, only)
+  predicate = filterlib.to_predicate(only)
+
+  if all_duplicates := find_duplicates(node, only=only):
+    duplicates_strs = '\n  ---'
+    for node_duplicates in all_duplicates:
+      for path in node_duplicates:
+        path_str = '/'.join(map(str, path))
+        duplicates_strs += f'\n  {path_str}'
+      duplicates_strs += '\n  ---'
+    raise ValueError(f'Found duplicate at paths:{duplicates_strs}')
+
+  def _to_hijax(jax_path, x):
+    if predicate(to_nnx_path(jax_path), x):
+      assert isinstance(x, variablelib.Variable)
+      x = x.copy()
+      x.set_raw_value(hijax.Box(x.get_raw_value()))
+    return x
+
+  node = jax.tree.map_with_path(
+    _to_hijax, node, is_leaf=lambda x: isinstance(x, variablelib.Variable)
+  )
+  return node
+
+
+def _is_hijax_variable(path, x):
+  return isinstance(x, variablelib.Variable) and x.mode == 'hijax'
+
+def to_lojax(
+  node: A, /, *, allow_duplicates: bool = False, only: filterlib.Filter = ...
+) -> A:
+  """ """
+  only = filterlib.All(_is_hijax_variable, only)
+  predicate = filterlib.to_predicate(only)
+
+  if not allow_duplicates and (
+    all_duplicates := find_duplicates(node, only=only)
+  ):
+    duplicates_strs = '\n  ---'
+    for node_duplicates in all_duplicates:
+      for path in node_duplicates:
+        path_str = '/'.join(map(str, path))
+        duplicates_strs += f'\n  {path_str}'
+      duplicates_strs += '\n  ---'
+    raise ValueError(f'Found duplicate at paths:{duplicates_strs}')
+
+  def _to_lojax(jax_path, x):
+    if predicate(to_nnx_path(jax_path), x):
+      assert isinstance(x, variablelib.Variable)
+      x = x.copy()
+      box = x.get_raw_value()
+      assert isinstance(box, hijax.Box)
+      x.set_raw_value(box.get())  # unwrap hijax.Box
+    return x
+
+  node = jax.tree.map_with_path(
+    _to_lojax, node, is_leaf=lambda x: isinstance(x, variablelib.Variable)
+  )
+  return node
+
+
 def pure(tree: A) -> A:
   """Returns a new tree with all ``Variable`` objects replaced with inner values.
 
@@ -2787,7 +2825,7 @@ def pure(tree: A) -> A:
 
   def _pure_fn(x):
     if isinstance(x, Variable):
-      return x.raw_value
+      return x.get_raw_value()
     return x
 
   return jax.tree.map(
@@ -3072,7 +3110,7 @@ def _key_path_to_key(key: tp.Any) -> Key:
     return str(key)
 
 
-def jax_to_nnx_path(jax_path: tuple, /):
+def to_nnx_path(jax_path: tuple, /):
   return tuple(_key_path_to_key(part) for part in jax_path)
 
 
diff --git a/flax/nnx/module.py b/flax/nnx/module.py
index aa32a7edf..83fafd587 100644
--- a/flax/nnx/module.py
+++ b/flax/nnx/module.py
@@ -182,7 +182,7 @@ def sow(
           f"Expected '{name}' to be of type '{variable_type.__name__}', "
           f"got '{type(variable).__name__}'"
         )
-      variable.raw_value = reduce_fn(variable.raw_value, value)
+      variable.set_value(reduce_fn(variable.get_value(), value))
     else:
       reduced_value = reduce_fn(init_fn(), value)
       setattr(self, name, variable_type(reduced_value))
diff --git a/flax/nnx/nn/normalization.py b/flax/nnx/nn/normalization.py
index 17a164ebf..b6a164adf 100644
--- a/flax/nnx/nn/normalization.py
+++ b/flax/nnx/nn/normalization.py
@@ -368,7 +368,7 @@ def __call__(
         mask=mask,
       )
       # stop_gradient only for flax_array_ref
-      if self.mean.has_ref or self.var.has_ref:
+      if self.mean.is_hijax or self.var.is_hijax:
         stop_gradient = jax.lax.stop_gradient
       else:
         stop_gradient = lambda x: x
diff --git a/flax/nnx/pytreelib.py b/flax/nnx/pytreelib.py
index 2d4d2e646..46663cb4b 100644
--- a/flax/nnx/pytreelib.py
+++ b/flax/nnx/pytreelib.py
@@ -238,7 +238,7 @@ def _collect_stats(
     var_type = type(node)
     if issubclass(var_type, nnx.RngState):
       var_type = nnx.RngState
-    size_bytes = SizeBytes.from_any(node.raw_value)
+    size_bytes = SizeBytes.from_any(node.get_value())
     if size_bytes:
       stats[var_type] = size_bytes
 
@@ -355,6 +355,7 @@ def _graph_node_meta_call(cls: tp.Type[P], *args, **kwargs) -> P:
   return node
 
 
+@jax.tree_util.register_static
 @dataclasses.dataclass(frozen=True, repr=False)
 class ArrayRepr(reprlib.Representable):
   shape: tp.Tuple[int, ...]
@@ -507,12 +508,8 @@ def _setattr(self, name, value: tp.Any) -> None:
       vars(self)[name] = value
 
   def _check_value(self, key, value, new_status: AttributeStatus | None):
-    def _has_arrays(leaves):
-      return any(
-          isinstance(leaf, (np.ndarray, jax.Array))
-          or variablelib.is_array_ref(leaf)
-          for leaf in leaves
-      )
+    def _has_data(leaves):
+      return any(is_data(leaf) for leaf in leaves)
 
     def _get_annotations(leaves):
       return {
@@ -547,7 +544,7 @@ def _has_visited(x):
           f' _.{key} = nnx.data(...)\n\n'
       )
 
-    if _has_arrays(leaves):
+    if _has_data(leaves):
       # check no data in nnx.static assignments
       if new_status is not None:
         if not new_status.is_data and new_status.explicit:
@@ -663,7 +660,7 @@ def __nnx_repr__(self):
         def to_shape_dtype(value):
           if isinstance(value, Variable):
             return value.replace(
-              raw_value=jax.tree.map(to_shape_dtype, value.raw_value)
+              value=jax.tree.map(to_shape_dtype, value.get_value())
             )
           elif variablelib.is_array_ref(value) and np.prod(value.shape) > 1:
             return MutableArrayRepr(value.shape, value.dtype)
diff --git a/flax/nnx/rnglib.py b/flax/nnx/rnglib.py
index 4489b6431..b48a61d46 100644
--- a/flax/nnx/rnglib.py
+++ b/flax/nnx/rnglib.py
@@ -23,7 +23,6 @@
 from flax import errors, struct
 from flax import typing
 from flax.nnx import graph
-from flax.nnx import variablelib
 from flax.nnx.nn import initializers
 from flax.nnx.variablelib import Variable
 from flax.nnx import filterlib
@@ -116,7 +115,7 @@ def __init__(
     self.count = RngCount(count, tag=tag)
 
   def __call__(self) -> jax.Array:
-    if not self.count.has_ref and not self.count._trace_state.is_valid():
+    if not self.count.trace_state.is_valid():
       raise errors.TraceContextError(
         f'Cannot mutate {type(self).__name__} from a different trace level'
       )
@@ -826,14 +825,11 @@ def split_rngs_wrapper(*args, **kwargs):
       and predicate((*path, 'count'), stream.count)
     ):
       key = stream()
-      backups.append((stream, stream.key.raw_value, stream.count.raw_value))
+      backups.append((stream, stream.key[...], stream.count[...]))
       key = random.split(key, splits)
       if squeeze:
         key = key[0]
-      if variablelib.is_array_ref(stream.key.raw_value):
-        stream.key.raw_value = variablelib.new_ref(key)  # type: ignore[assignment]
-      else:
-        stream.key.value = key
+      stream.key.set_value(key)
       if squeeze:
         counts_shape = stream.count.shape
       elif isinstance(splits, int):
@@ -841,11 +837,7 @@ def split_rngs_wrapper(*args, **kwargs):
       else:
         counts_shape = (*splits, *stream.count.shape)
 
-      count = jnp.zeros(counts_shape, dtype=jnp.uint32)
-      if variablelib.is_array_ref(stream.count.raw_value):
-        stream.count.raw_value = variablelib.new_ref(count)  # type: ignore[assignment]
-      else:
-        stream.count.value = count
+      stream.count.set_value(jnp.zeros(counts_shape, dtype=jnp.uint32))
 
   return SplitBackups(backups)
 
@@ -992,10 +984,10 @@ def fork_rngs_wrapper(*args, **kwargs):
       ):
         forked_stream = stream.fork(split=splits)
         # backup the original stream state
-        backups.append((stream, stream.key.raw_value, stream.count.raw_value))
+        backups.append((stream, stream.key[...], stream.count[...]))
         # apply the forked key and count to the original stream
-        stream.key.raw_value = forked_stream.key.raw_value
-        stream.count.raw_value = forked_stream.count.raw_value
+        stream.key.set_value(forked_stream.key.get_value())
+        stream.count.set_value(forked_stream.count.get_value())
 
   return SplitBackups(backups)
 
@@ -1004,7 +996,7 @@ def backup_keys(node: tp.Any, /):
   backups: list[StreamBackup] = []
   for _, stream in graph.iter_graph(node):
     if isinstance(stream, RngStream):
-      backups.append((stream, stream.key.raw_value))
+      backups.append((stream, stream.key[...]))
   return backups
 
 def _scalars_only(
@@ -1090,13 +1082,13 @@ def reseed(
       if stream.key.tag in stream_keys:
         key = rngs[stream.key.tag]()
         key = policy(path, key, stream.key.shape)
-        stream.key[...] = key
-        stream.count[...] = jnp.zeros(key.shape, dtype=jnp.uint32)
+        stream.key.set_value(key)
+        stream.count.set_value(jnp.zeros(key.shape, dtype=jnp.uint32))
 
 
 def restore_rngs(backups: tp.Iterable[StreamBackup], /):
   for backup in backups:
     stream = backup[0]
-    stream.key.raw_value = backup[1]
+    stream.key.set_value(backup[1])
     if len(backup) == 3:
-      stream.count.raw_value = backup[2]  # count
+      stream.count.set_value(backup[2])  # count
diff --git a/flax/nnx/statelib.py b/flax/nnx/statelib.py
index 5cbab6394..2a11b325e 100644
--- a/flax/nnx/statelib.py
+++ b/flax/nnx/statelib.py
@@ -491,7 +491,7 @@ def to_pure_dict(
 ) -> dict[str, tp.Any]:
   # Works for nnx.Variable
   if extract_fn is None:
-    extract_fn = lambda x: x.value if isinstance(x, variablelib.Variable) else x
+    extract_fn = lambda x: x.get_value() if isinstance(x, variablelib.Variable) else x
   flat_values = {k: extract_fn(x) for k, x in to_flat_state(state)}
   return traversals.unflatten_mapping(flat_values)
 
@@ -767,6 +767,6 @@ def create_path_filters(state: State):
   value_paths: dict[tp.Any, set[PathParts]] = {}
   for path, value in flat_state:
     if isinstance(value, variablelib.Variable):
-      value = value.raw_value
+      value = value.get_value()
     value_paths.setdefault(value, set()).add(path)
   return {filterlib.PathIn(*value_paths[value]): value for value in value_paths}
\ No newline at end of file
diff --git a/flax/nnx/summary.py b/flax/nnx/summary.py
index 000561985..7d9d8f9ef 100644
--- a/flax/nnx/summary.py
+++ b/flax/nnx/summary.py
@@ -96,7 +96,7 @@ def _collect_stats(
       var_type = type(value)
       if issubclass(var_type, nnx.RngState):
         var_type = nnx.RngState
-      size_bytes = SizeBytes.from_any(value.value)
+      size_bytes = SizeBytes.from_any(value.get_value())
       if var_type in stats:
         stats[var_type] += size_bytes
       else:
@@ -455,11 +455,13 @@ def do_vjp(*args, **kwargs):
 
     for var_type in variable_types:
       attributes = {}
+      variable: variablelib.Variable
       for name, variable in node_info.variable_groups[var_type].items():
-        value = variable.value
+        value = variable.get_value()
         value_repr = _render_array(value) if _has_shape_dtype(value) else ''
         metadata = variable.get_metadata()
-
+        metadata.pop('is_hijax')
+        metadata.pop('eager_sharding', None)
         if metadata:
           attributes[name] = {
             'value': value_repr,
diff --git a/flax/nnx/training/optimizer.py b/flax/nnx/training/optimizer.py
index 586f64d7f..2bd1068cb 100644
--- a/flax/nnx/training/optimizer.py
+++ b/flax/nnx/training/optimizer.py
@@ -30,6 +30,11 @@
 
 # TODO: add tests and docstrings
 
+def _to_pure_lojax(x):
+  x = nnx.pure(x)
+  x = nnx.to_arrays(x, allow_duplicates=True)
+  x = nnx.to_lojax(x, allow_duplicates=True)
+  return x
 
 
 class OptState(Variable):
@@ -53,7 +58,7 @@ class OptVariable(OptState):
 def to_opt_state(tree):
   def _to_opt_state(x):
     if isinstance(x, Variable):
-      opt_state = OptVariable(x.value, **x.get_metadata())  # type: ignore
+      opt_state = OptVariable(x.get_value(), **x.get_metadata())  # type: ignore
     else:
       opt_state = OptArray(x)
     return opt_state
@@ -210,10 +215,10 @@ def update(self, model: M, grads, /, **kwargs):
       **kwargs: additional keyword arguments passed to the tx.update, to support
       ``GradientTransformationExtraArgs``, such as ``optax.scale_by_backtracking_linesearch``.
     """
-    param_arrays = nnx.to_arrays(nnx.pure(nnx.state(model, self.wrt)))
-    grad_arrays = nnx.to_arrays(nnx.pure(nnx.state(grads)))
-    opt_state_arrays = nnx.to_arrays(nnx.pure(self.opt_state))
-    kwargs_arrays = nnx.to_arrays(nnx.pure(kwargs))
+    param_arrays = _to_pure_lojax(nnx.state(model, self.wrt))
+    grad_arrays = _to_pure_lojax(nnx.state(grads, self.wrt))
+    opt_state_arrays = _to_pure_lojax(self.opt_state)
+    kwargs_arrays = _to_pure_lojax(kwargs)
 
     updates, new_opt_state = self.tx.update(
       grad_arrays, opt_state_arrays, param_arrays, **kwargs_arrays
diff --git a/flax/nnx/transforms/compilation.py b/flax/nnx/transforms/compilation.py
index d08d83335..e493bc9a4 100644
--- a/flax/nnx/transforms/compilation.py
+++ b/flax/nnx/transforms/compilation.py
@@ -30,7 +30,7 @@
   statelib,
   variablelib,
 )
-from flax.typing import MISSING, Missing
+from flax.typing import MISSING, Missing, PathParts
 
 F = tp.TypeVar('F', bound=tp.Callable[..., tp.Any])
 P = tp.ParamSpec('P')
@@ -71,7 +71,7 @@ def shardings(self) -> tuple[tp.Any, ...]:
     return self._shardings
 
   def map_prefix(
-    self, path: variablelib.PathParts, variable: variablelib.Variable
+    self, path: PathParts, variable: variablelib.Variable
   ) -> tp.Any:
     for filter, sharding in zip(self.filters, self.shardings):
       predicate = filterlib.to_predicate(filter)
diff --git a/flax/nnx/transforms/transforms.py b/flax/nnx/transforms/transforms.py
index 54027c6ff..132ca2b0e 100644
--- a/flax/nnx/transforms/transforms.py
+++ b/flax/nnx/transforms/transforms.py
@@ -146,7 +146,9 @@ def eval_shape(
   def _eval_shape_fn(*args, **kwargs):
     args, kwargs = extract.from_tree((args, kwargs))
     out = f(*args, **kwargs)
-    return graph.to_arrays(extract.to_tree(out), allow_duplicates=True)
+    return graph.to_lojax(
+      graph.to_arrays(extract.to_tree(out), allow_duplicates=True)
+    )
 
   out = jax.eval_shape(_eval_shape_fn, *args, **kwargs)
   return extract.from_tree(out)
diff --git a/flax/nnx/variablelib.py b/flax/nnx/variablelib.py
index 6d4c358a1..9601bf681 100644
--- a/flax/nnx/variablelib.py
+++ b/flax/nnx/variablelib.py
@@ -14,29 +14,32 @@
 # pytype: skip-file
 from __future__ import annotations
 
-import contextlib
 import dataclasses
 import functools
 from functools import partial
 import threading
 import typing as tp
 from typing import Any
+import warnings
+
 from flax import config
+from jax._src import hijax
 
 import jax
 import treescope  # type: ignore[import-untyped]
 
 from flax import errors
 from flax.core import spmd as core_spmd
-from flax.nnx import filterlib, reprlib, tracers, visualization
-from flax.typing import MISSING, Missing, PathParts, SizeBytes
+from flax.nnx import reprlib, tracers, visualization
+from flax.typing import MISSING, Missing, SizeBytes
 import jax.tree_util as jtu
-import jax.numpy as jnp
 from jax._src.state.types import AbstractRef
 
 A = tp.TypeVar('A')
 B = tp.TypeVar('B')
+C = tp.TypeVar('C')
 F = tp.TypeVar('F', bound=tp.Callable[..., tp.Any])
+P = tp.TypeVar('P', bound=property)
 V = tp.TypeVar('V', bound='Variable[Any]')
 GetValueHook = tp.Callable[['Variable[A]', A], A]
 SetValueHook = tp.Callable[['Variable[A]', A], A]
@@ -50,109 +53,77 @@
 # The following ensures we avoid an ImportError or DeprecationWarning.
 if hasattr(jax, 'new_ref') and hasattr(jax, 'Ref'):
   # JAX v0.7.2 or newer
-  from jax import new_ref
   from jax import Ref
 elif hasattr(jax, 'array_ref') and hasattr(jax, 'ArrayRef'):
   # JAX v0.7.1
-  from jax import array_ref as new_ref # type: ignore[import-untyped]
   from jax import ArrayRef as Ref  # type: ignore[import-untyped]
 else:
   # JAX v0.7.0 or older
-  from jax.experimental import mutable_array as new_ref
   from jax.experimental import MutableArray as Ref
 
 @dataclasses.dataclass
 class VariableContext(threading.local):
-  mutable_variable_stack: list[bool] = dataclasses.field(default_factory=list)
+  variable_mode_stack: list[tp.Literal['lojax', 'hijax', 'ref']] = (
+    dataclasses.field(default_factory=list)
+  )
 
 
 VARIABLE_CONTEXT = VariableContext()
 
 
-def using_refs() -> bool:
-  """Returns whether Variables are using ArrayRefs by default.
+def current_variable_mode() -> tp.Literal['lojax', 'hijax', 'ref']:
+  """ """
+  if VARIABLE_CONTEXT.variable_mode_stack:
+    return VARIABLE_CONTEXT.variable_mode_stack[-1]
+  match config.flax_variable_mode:
+    case 'lojax' | 'hijax' | 'ref':
+      return config.flax_variable_mode
+    case other:
+      raise ValueError(f'Unrecognized variable mode: {other}')
 
-  Example::
 
-    >>> from flax import nnx
-    ...
-    >>> nnx.using_refs()
-    False
-    >>> nnx.use_refs(True)
-    <...>
-    >>> nnx.using_refs()
-    True
-    >>> nnx.use_refs(False)
-    <...>
-    >>> nnx.using_refs()
-    False
-
-
-  Returns:
-    A boolean indicating if Variables are using ArrayRefs by default.
-  """
-  if VARIABLE_CONTEXT.mutable_variable_stack:
-    return VARIABLE_CONTEXT.mutable_variable_stack[-1]
+def variable_mode(value: tp.Literal['lojax', 'hijax', 'ref'], /):
+  """ """
+  if VARIABLE_CONTEXT.variable_mode_stack:
+    prev_value = VARIABLE_CONTEXT.variable_mode_stack[-1]
+    VARIABLE_CONTEXT.variable_mode_stack[-1] = value
   else:
-    return config.flax_array_ref
+    prev_value = None
+    VARIABLE_CONTEXT.variable_mode_stack.append(value)
+  return ModeContext(prev_value, value)
 
 
-def use_refs(value: bool, /):
-  """Sets whether Variables should use ArrayRefs by default or not.
+class ModeContext:
+  def __init__(
+    self,
+    prev_value: tp.Literal['lojax', 'hijax', 'ref'] | None,
+    new_value: tp.Literal['lojax', 'hijax', 'ref'],
+  ):
+    self.prev_value: tp.Literal['lojax', 'hijax', 'ref'] | None = prev_value
+    self.new_value: tp.Literal['lojax', 'hijax', 'ref'] = new_value
 
-  Example usage::
+  def __enter__(self):
+    if self.prev_value is not None:
+      VARIABLE_CONTEXT.variable_mode_stack.insert(-1, self.prev_value)
 
-    >>> from flax import nnx
-    >>> # Use ArrayRefs by default
-    >>> nnx.use_refs(True)
-    <...>
-    >>> # Variable will now use ArrayRefs
-    >>> v = nnx.Variable(jax.numpy.ones((2, 3)))
-    >>> v.has_ref
-    True
-    >>> v.raw_value
-    Ref(...)
-    >>> nnx.use_refs(False)
-    <...>
-
-  It can also be used as a context manager to temporarily
-  change the default behavior for a block of code::
-
-    >>> nnx.use_refs(False)
-    <...>
-    >>> with nnx.use_refs(True):
-    ...   v = nnx.Variable(jax.numpy.ones((2, 3)))
-    ...   v.has_ref
-    True
-    >>> # it will reset outside
-    >>> v = nnx.Variable(jax.numpy.ones((2, 3)))
-    >>> v.has_ref
-    False
-
-  Args:
-    value: A boolean indicating if Variables should use ArrayRefs by default.
-
-  Returns:
-    A context manager that resets the context to the previous value.
-  """
-  # prev_value = VARIABLE_CONTEXT.mutable_variable_stack[-1] if VARIABLE_CONTEXT.mutable_variable_stack else None
-  # VARIABLE_CONTEXT.mutable_variable_stack.append(value)
-  if VARIABLE_CONTEXT.mutable_variable_stack:
-    prev_value = VARIABLE_CONTEXT.mutable_variable_stack[-1]
-    VARIABLE_CONTEXT.mutable_variable_stack[-1] = value
-  else:
-    prev_value = None
-    VARIABLE_CONTEXT.mutable_variable_stack.append(value)
-  return _clean_mutable_arrays_context(prev_value)
+  def __exit__(self, exc_type, exc_value, traceback):
+    VARIABLE_CONTEXT.variable_mode_stack.pop()
 
-@contextlib.contextmanager
-def _clean_mutable_arrays_context(prev_value: bool | None):
-  if prev_value is not None:
-    VARIABLE_CONTEXT.mutable_variable_stack.insert(-1, prev_value)
-  try:
-    yield
-  finally:
-    VARIABLE_CONTEXT.mutable_variable_stack.pop()
+  def __call__(self, f: F) -> F:
+    # undo eager stack change
+    VARIABLE_CONTEXT.variable_mode_stack.pop()
+    if self.prev_value is not None:
+      VARIABLE_CONTEXT.variable_mode_stack.append(self.prev_value)
+
+    @functools.wraps(f)
+    def set_variable_mode_wrapper(*args, **kwargs):
+      VARIABLE_CONTEXT.variable_mode_stack.append(self.new_value)
+      try:
+        return f(*args, **kwargs)
+      finally:
+        VARIABLE_CONTEXT.variable_mode_stack.pop()
+
+    return set_variable_mode_wrapper  # type: ignore[return-value]
 
 
 def is_array_ref(x) -> tp.TypeGuard[Ref]:
@@ -172,6 +143,38 @@ class VariableMetadata(tp.Generic[A]):
   metadata: tp.Mapping[str, tp.Any] = dataclasses.field(default_factory=dict)
 
 
+# --------------------------------------------
+# Variable
+# --------------------------------------------
+
+
+def _variable_operator(name: str) -> tp.Callable[[Variable[A], tp.Any], A]:
+  def variable_operator_method(self, other):
+    value = self.get_value()
+    if isinstance(other, Variable):
+      other = other.get_value()
+    return getattr(value, name)(other)
+
+  variable_operator_method.__name__ = name
+  return variable_operator_method
+
+
+def _variable_unary_operator(name: str) -> tp.Callable[[Variable[A]], A]:
+  def variable_unary_operator_method(self):
+    value = self.get_value()
+    return getattr(value, name)()
+
+  variable_unary_operator_method.__name__ = name
+  return variable_unary_operator_method
+
+@dataclasses.dataclass(frozen=True)
+class BoxRepr(reprlib.Representable):
+  box: hijax.Box
+
+  def __nnx_repr__(self):
+    yield reprlib.Object(type=type(self.box).__name__)
+    yield reprlib.Attr('value', self.box.get())
+
 class VariableMeta(type):
   def __new__(cls, cls_name, bases, attrs):
     if '__slots__' not in attrs:
@@ -239,32 +242,61 @@ class Variable(tp.Generic[A], reprlib.Representable, metaclass=VariableMeta):
     })
   """
 
-  __slots__ = ('raw_value', '_trace_state', '_var_metadata')
-
-  raw_value: A
+  __slots__ = ('_raw_value', '_trace_state', '_var_metadata')
+  _raw_value: A
   _trace_state: tracers.TraceState
   _var_metadata: dict[str, tp.Any]
 
+  @property
+  def mode(self) -> tp.Literal['lojax', 'hijax', 'ref']:
+    if isinstance(self._raw_value, hijax.Box):
+      return 'hijax'
+    elif is_array_ref(self._raw_value):
+      return 'ref'
+    else:
+      return 'lojax'
+
+  @property
+  def shape(self: Variable[jax.Array]) -> tuple[int, ...]:
+    return self.get_value().shape
+
   def __init__(
     self,
-    value: tp.Union[A, VariableMetadata[A]],
+    value: A | VariableMetadata[A],
     *,
-    use_ref: bool | None = None,
+    mode: tp.Literal['lojax', 'hijax', 'ref'] | None = None,
+    eager_sharding: bool | None = None,
     **metadata: tp.Any,
   ):
-    if use_ref is None:
-      use_ref = using_refs()
-
     var_t = type(self)
     object.__setattr__(self, '_trace_state', tracers.TraceState())
 
     if isinstance(value, VariableMetadata):
-      metadata.update(value.metadata)
+      aux_metadata = dict(value.metadata)
+      if 'mode' in aux_metadata:
+        if mode is not None and mode != aux_metadata['mode']:
+          raise ValueError(
+            'Cannot specify mode both in VariableMetadata and as an '
+            'argument to Variable constructor.'
+          )
+        mode = aux_metadata.pop('mode')
+      if 'eager_sharding' in aux_metadata:
+        if (
+          eager_sharding is not None
+          and eager_sharding != aux_metadata['eager_sharding']
+        ):
+          raise ValueError(
+            'Cannot specify eager_sharding both in VariableMetadata and as '
+            'an argument to Variable constructor.'
+          )
+        eager_sharding = aux_metadata.pop('eager_sharding')
+      metadata.update(aux_metadata)
       value = tp.cast(A, value.raw_value)
-    elif is_array_ref(value):
-      raise ValueError('Cannot pass a ArrayRef directly into Variable init.')
 
-    object.__setattr__(self, 'raw_value', value)
+    if any(is_array_ref(v) for v in jax.tree.leaves(value)):
+      raise ValueError('Cannot pass a Ref directly into Variable constructor.')
+
+    object.__setattr__(self, '_raw_value', value)
 
     if hasattr(var_t, 'on_get_value') and 'on_get_value' not in metadata:
       metadata['on_get_value'] = var_t.on_get_value
@@ -284,34 +316,49 @@ def __init__(
     if 'sharding' in metadata:
       metadata['sharding_names'] = metadata.pop('sharding')
 
-    object.__setattr__(self, '_var_metadata', metadata)
     # run create_value hooks
-    value = self.create_value(self.raw_value)
+    if 'on_create_value' in metadata:
+      value = metadata['on_create_value'](self, value)
+
+    if eager_sharding is None:
+      eager_sharding = config.flax_always_shard_variable
+
+    if mode is None:
+      mode = current_variable_mode()
 
-    # shard the value if applicable
-    do_eager_sharding = config.flax_always_shard_variable
-    if 'eager_sharding' in metadata:
-      do_eager_sharding = metadata['eager_sharding']
-    if do_eager_sharding and 'sharding_names' in metadata:
+    metadata['mode'] = mode
+    object.__setattr__(self, '_var_metadata', metadata)
+    object.__setattr__(self, '_raw_value', value)
+    # run create_value hook
+    value = self.create_value(value)  # type: ignore
+    # shard the _value if applicable
+    if eager_sharding and 'sharding_names' in metadata:
+      metadata['eager_sharding'] = eager_sharding
       value = core_spmd.shard_value(
-        value, metadata['sharding_names'], metadata.get('sharding_rules', None),
-        metadata.get('mesh', None))
+        value,
+        metadata['sharding_names'],
+        metadata.get('sharding_rules', None),
+        metadata.get('mesh', None),
+      )
+
+    if mode == 'hijax':
+      value = hijax.Box(value)  # type: ignore
+    elif mode == 'ref':
+      value = jax.new_ref(value)  # type: ignore
 
-    # Create the ref out of the array value
-    if use_ref:
-      value = new_ref(jnp.asarray(value))  # type: ignore[assignment]  # type: ignore[assignment]
+    object.__setattr__(self, '_raw_value', value)
 
-    object.__setattr__(self, 'raw_value', value)
+  @property
+  def trace_state(self) -> tracers.TraceState:
+    return self._trace_state
 
   def __getattr__(self, name: str) -> tp.Any:
     if name in object.__getattribute__(self, '_var_metadata'):
       return self._var_metadata[name]
-    return getattr(self.raw_value, name)
+    return getattr(self._raw_value, name)
 
   def __setattr__(self, name: str, value: tp.Any):
-    if not self._trace_state.is_valid() and (
-      name != 'value' or not self.has_ref
-    ):
+    if not self._trace_state.is_valid():
       raise errors.TraceContextError(
         f'Cannot mutate {type(self).__name__} from a different trace level'
       )
@@ -330,32 +377,21 @@ def __delattr__(self, name: str):
       raise errors.TraceContextError(
         f'Cannot mutate {type(self).__name__} from a different trace level'
       )
-
-    if (
-      name == 'value'
-      or name == 'raw_value'
-      or name == '_var_metadata'
-      or name == '_trace_state'
-    ):
+    try:
       object.__delattr__(self, name)
-    else:
-      del self._var_metadata[name]
+    except AttributeError as e:
+      raise AttributeError(
+        f'Cannot delete attribute {name}. '
+        f'To delete Variable metadata use:\n\n'
+        f"  variable.del_metadata('{name}')"
+      ) from e
 
   # NOTE(cgarciae): adding this for backward compatibility with VariableState
   @property
   def type(self):
     """The type of the variable."""
-    import warnings
-    warnings.warn(
-      "'.type' is deprecated, use 'type(variable)' instead.",
-      DeprecationWarning,
-      stacklevel=2,
-    )
-    return type(self)
 
-  @property
-  def has_ref(self) -> bool:
-    return is_array_ref(self.raw_value)
+    return type(self)
 
   @tp.overload
   def get_metadata(self) -> dict[str, tp.Any]: ...
@@ -372,11 +408,12 @@ def get_metadata(
       default: The default value to return if the metadata key is not found. If
         not provided and the key is not found, raises a KeyError.
     """
+    metadata = self._var_metadata.copy()
     if name is None:
-      return self._var_metadata
-    if name not in self._var_metadata and not isinstance(default, Missing):
+      return metadata
+    if name not in metadata and not isinstance(default, Missing):
       return default
-    return self._var_metadata[name]
+    return metadata[name]
 
   @tp.overload
   def set_metadata(self, metadata: dict[str, tp.Any], /) -> None: ...
@@ -405,11 +442,28 @@ def set_metadata(self, *args, **kwargs) -> None:
         'Cannot mix positional and keyword arguments in set_metadata'
       )
     if len(args) == 1:
-      self._var_metadata = dict(args[0])
+      metadata = dict(args[0])
+      if 'mode' not in metadata:
+        raise ValueError('metadata is missing required key `mode` key')
+      if metadata['mode'] != self.mode:
+        raise ValueError(
+          f'Cannot change `mode` metadata, expected {self.mode}, '
+          f'got {metadata["mode"]}'
+        )
+      self._var_metadata = metadata
     elif len(args) == 2:
       name, value = args
+      if name == 'mode' and value != self.mode:
+        raise ValueError(
+          f'Cannot change `mode` metadata, expected {self.mode}, got {value}'
+        )
       self._var_metadata[name] = value
     elif kwargs:
+      if 'mode' in kwargs and kwargs['mode'] != self.mode:
+        raise ValueError(
+          f'Cannot change `mode` metadata, expected {self.mode}, '
+          f'got {kwargs["mode"]}'
+        )
       self._var_metadata.update(kwargs)
     else:
       raise TypeError(
@@ -417,6 +471,20 @@ def set_metadata(self, *args, **kwargs) -> None:
         f'got args={args}, kwargs={kwargs}'
       )
 
+  def del_metadata(self, name: str) -> None:
+    """Delete a metadata entry for the Variable.
+
+    Args:
+      name: The key of the metadata element to delete.
+    """
+    if not self._trace_state.is_valid():
+      raise errors.TraceContextError(
+        f'Cannot mutate {type(self).__name__} from a different trace level'
+      )
+    if name == 'mode':
+      raise ValueError('Cannot delete `mode` metadata')
+    del self._var_metadata[name]
+
   def copy_from(self, other: Variable[A]) -> None:
     if type(self) is not type(other):
       raise ValueError(
@@ -425,50 +493,103 @@ def copy_from(self, other: Variable[A]) -> None:
       )
     if self is other:
       return
-    self.raw_value = other.raw_value
+    self._raw_value = other._raw_value
     self._var_metadata.clear()
     self._var_metadata.update(other.get_metadata())
 
   def update_from_state(self, variable_state: Variable[A]):
-    if self.has_ref and (
-      variable_state.has_ref or isinstance(variable_state.raw_value, jax.Array)
-    ):
-      self.raw_value[...] = variable_state.raw_value[...] # type: ignore
-    else:
-      object.__setattr__(self, 'raw_value', variable_state.raw_value)
+    object.__setattr__(self, '_raw_value', variable_state._raw_value)
 
     if self._var_metadata != variable_state._var_metadata:
-      object.__setattr__(
-        self, '_var_metadata', variable_state._var_metadata.copy()
+      metadata = variable_state.get_metadata().copy()
+      metadata['mode'] = self.mode
+      object.__setattr__(self, '_var_metadata', metadata)
+
+  @tp.final
+  def get_raw_value(self) -> A:
+    return self._raw_value
+
+  @tp.final
+  def set_raw_value(self, value: A):
+    if not self._trace_state.is_valid():
+      raise errors.TraceContextError(
+        f'Cannot mutate {type(self).__name__} from a different trace level'
       )
+    object.__setattr__(self, '_raw_value', value)
+
+  @property
+  def raw_value(self) -> A:
+    warnings.warn(
+      "'.raw_value' access is now deprecated. Use:\n\n"
+      '  variable.get_raw_value()\n',
+      DeprecationWarning,
+      stacklevel=2,
+    )
+    return self.get_raw_value()
+
+  @raw_value.setter
+  def raw_value(self, value: A):
+    warnings.warn(
+      "'.raw_value' access is now deprecated. Use:\n\n"
+      '  variable.set_raw_value(value)\n',
+      DeprecationWarning,
+      stacklevel=2,
+    )
+    self.set_raw_value(value)
 
   @property
   def value(self) -> A:
-    value = self.raw_value
+    warnings.warn(
+      "'.value' access is now deprecated. For Variable[Array] instances use:\n\n"
+      '  variable[...]\n\n'
+      'For other Variable types use:\n\n'
+      '  variable.get_value()\n',
+      DeprecationWarning,
+      stacklevel=2,
+    )
+    value = self._raw_value
     if is_array_ref(value):
       value = value[...]
 
+    return self.get_value()
+
+  @value.setter
+  def value(self, value: A):
+    warnings.warn(
+      "'.value' access is now deprecated. For Variable[Array] instances use:\n\n"
+      '  variable[...] = value\n\n'
+      'For other Variable types use:\n\n'
+      '  variable.set_value(value)\n',
+      DeprecationWarning,
+      stacklevel=2,
+    )
+    self.set_value(value)
+
+  def create_value(self, value: A):
+    return value
+
+  def get_value(self) -> A:
+    if isinstance(self._raw_value, hijax.Box):
+      value = self._raw_value.get()
+    else:
+      value = jax.tree.map(lambda x: x, self._raw_value)  # make a copy
     if 'on_get_value' in self._var_metadata:
       value = self._var_metadata['on_get_value'](self, value)
     return value
 
-  @value.setter
-  def value(self, value: A):
+  def set_value(self, value: A):
+    value = jax.tree.map(lambda x: x, value)  # make a copy
     if isinstance(value, Variable):
       raise ValueError(
         'Cannot set value to a Variable, use `copy_from` method instead'
       )
     if 'on_set_value' in self._var_metadata:
       value = self._var_metadata['on_set_value'](self, value)
-    if self.has_ref:
-      self.raw_value[...] = value  # type: ignore
-    else:
-      object.__setattr__(self, 'raw_value', value)
 
-  def create_value(self, value: A):
-    if 'on_create_value' in self._var_metadata:
-      value = self._var_metadata['on_create_value'](self, value)
-    return value
+    if isinstance(self._raw_value, hijax.Box):
+      self._raw_value.set(value)
+    else:
+      object.__setattr__(self, '_raw_value', value)
 
   def add_axis(self, axis_index: AxisIndex, axis_name: AxisName | None):
     if 'on_add_axis' in self._var_metadata:
@@ -479,27 +600,28 @@ def remove_axis(self, axis_index: AxisIndex, axis_name: AxisName | None):
       self._var_metadata['on_remove_axis'](self, axis_index, axis_name)
 
   @tp.overload
-  def replace(self, value: B, **kwargs) -> Variable[B]:
-    ...
+  def replace(self, value: B, **kwargs) -> Variable[B]: ...
 
   @tp.overload
-  def replace(self, **kwargs) -> Variable[A]:
-    ...
+  def replace(self, **kwargs) -> Variable[A]: ...
 
   def replace(self, value: tp.Any = Missing, **kwargs) -> Variable[tp.Any]:
     if value is not Missing:
-      kwargs['raw_value'] = value
+      kwargs['value'] = value
+
+    if 'is_hijax' in kwargs and kwargs['is_hijax'] != self.is_hijax:
+      raise ValueError(
+        f'Cannot change `is_hijax` metadata, expected {self.is_hijax}, '
+        f'got {kwargs["is_hijax"]}'
+      )
 
-    # rename `value` to `raw_value`
-    if 'value' in kwargs:
-      kwargs['raw_value'] = kwargs.pop('value')
+    if 'raw_value' in kwargs:
+      raise RuntimeError
 
     # return `value` if it is a Variable
-    if 'raw_value' in kwargs and isinstance(
-      value := kwargs['raw_value'], Variable
-    ):
+    if 'value' in kwargs and isinstance(value := kwargs['value'], Variable):
       # remove value from kwargs
-      kwargs.pop('raw_value')
+      kwargs.pop('value')
       if type(self) is not type(value):
         raise ValueError(
           'Cannot replace value from incompatible container, '
@@ -516,41 +638,59 @@ def replace(self, value: tp.Any = Missing, **kwargs) -> Variable[tp.Any]:
     # return new instance with updated attributes
     obj = object.__new__(type(self))
     object.__setattr__(obj, '_trace_state', self._trace_state)
-    object.__setattr__(obj, 'raw_value', kwargs.pop('raw_value'))
+    object.__setattr__(obj, '_raw_value', kwargs.pop('value'))
     object.__setattr__(obj, '_var_metadata', self.get_metadata() | kwargs)
     return obj
 
   @classmethod
-  def from_metadata(cls, value: A, attributes: dict[str, tp.Any]):
+  def _new(
+    cls,
+    value: A,
+    metadata: dict[str, tp.Any],
+  ) -> Variable[A]:
     obj = object.__new__(cls)
     object.__setattr__(obj, '_trace_state', tracers.TraceState())
-    object.__setattr__(obj, 'raw_value', value)
-    object.__setattr__(obj, '_var_metadata', attributes)
+    object.__setattr__(obj, '_raw_value', value)
+    object.__setattr__(obj, '_var_metadata', metadata)
     return obj
 
+  @classmethod
+  def from_metadata(
+    cls,
+    value: A,
+    attributes: dict[str, tp.Any],
+  ) -> Variable[A]:
+    obj = cls._new(value, dict(attributes))
+    return obj  # type: ignore[return-value]
+
   def copy(self: Variable[A]) -> Variable[A]:
     obj = object.__new__(type(self))
     object.__setattr__(obj, '_trace_state', tracers.TraceState())
-    object.__setattr__(obj, 'raw_value', self.raw_value)
+    object.__setattr__(obj, '_raw_value', self._raw_value)
     object.__setattr__(obj, '_var_metadata', self.get_metadata().copy())
     return obj
 
   to_state = copy
 
   def __nnx_repr__(self):
-    stats = SizeBytes.from_any(self.raw_value)
+    stats = SizeBytes.from_any(self._raw_value)
     if stats:
       comment = f' # {stats}'
     else:
       comment = ''
 
     yield reprlib.Object(type=type(self).__name__, comment=comment)
-    yield reprlib.Attr('value', self.raw_value)
+    if isinstance(self._raw_value, hijax.Box):
+      yield reprlib.Attr('value', BoxRepr(self._raw_value))
+    else:
+      yield reprlib.Attr('value', self._raw_value)
     for name, value in self._var_metadata.items():
+      if name == 'is_hijax' and value is False:
+        continue
       yield reprlib.Attr(name, value)
 
   def __treescope_repr__(self, path, subtree_renderer):
-    size_bytes = SizeBytes.from_any(self.value)
+    size_bytes = SizeBytes.from_any(self.get_value())
     if size_bytes:
       stats_repr = f' # {size_bytes}'
       first_line_annotation = treescope.rendering_parts.comment_color(
@@ -559,7 +699,7 @@ def __treescope_repr__(self, path, subtree_renderer):
     else:
       first_line_annotation = None
 
-    children = {'value': self.raw_value, **self._var_metadata}
+    children = {'value': self.get_value(), **self._var_metadata}
     return visualization.render_object_constructor(
       object_type=type(self),
       attributes=children,
@@ -586,18 +726,18 @@ def on_remove_axis(
     ) -> V: ...
 
   def __jax_array__(self):
-    return self.value
+    return self.get_value()
 
   # pickle support
   def __getstate__(self):
     return {
-      'raw_value': self.raw_value,
+      '_raw_value': self._raw_value,
       '_trace_state': self._trace_state,
       '_var_metadata': self._var_metadata,
     }
 
   def __setstate__(self, state):
-    object.__setattr__(self, 'raw_value', state['raw_value'])
+    object.__setattr__(self, '_raw_value', state['_raw_value'])
     object.__setattr__(self, '_trace_state', state['_trace_state'])
     object.__setattr__(self, '_var_metadata', state['_var_metadata'])
 
@@ -615,287 +755,161 @@ def __getitem__(self: Variable[tuple[B, ...]], key: int) -> B: ...
   @tp.overload
   def __getitem__(self, key) -> tp.Any: ...
   def __getitem__(self, key):
-    return self.value[key]  # type: ignore
+    return self.get_value()[key]  # type: ignore
 
   def __setitem__(self, key, item_value) -> None:
-    value = self.value
+    value = self.get_value()
     if isinstance(value, jax.Array):
       value = value.at[key].set(item_value)  # type: ignore[assignment]
     else:
       value[key] = item_value  # type: ignore
-    self.value = value  # type: ignore
+    self.set_value(value)  # type: ignore
+
+  def __delitem__(self, key) -> None:
+    value = self.get_value()
+    del value[key]  # type: ignore
+    self.set_value(value)  # type: ignore
 
   def __call__(self, *args, **kwargs) -> tp.Any:
-    return self.value(*args, **kwargs)  # type: ignore
+    return self.get_value()(*args, **kwargs)  # type: ignore
 
   def __len__(self) -> int:
-    return len(self.value)  # type: ignore
+    return len(self.get_value())  # type: ignore
 
   def __iter__(self) -> tp.Iterator:
-    return iter(self.value)  # type: ignore
+    return iter(self.get_value())  # type: ignore
 
   def __contains__(self, item) -> bool:
-    return item in self.value  # type: ignore
-
-  def __add__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__add__(other)  # type: ignore
-
-  def __sub__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__sub__(other)  # type: ignore
-
-  def __mul__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__mul__(other)  # type: ignore
-
-  def __matmul__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__matmul__(other)  # type: ignore
-
-  def __truediv__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__truediv__(other)  # type: ignore
-
-  def __floordiv__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__floordiv__(other)  # type: ignore
-
-  def __mod__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__mod__(other)  # type: ignore
-
-  def __divmod__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__divmod__(other)  # type: ignore
-
-  def __pow__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__pow__(other)  # type: ignore
-
-  def __lshift__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__lshift__(other)  # type: ignore
-
-  def __rshift__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rshift__(other)  # type: ignore
-
-  def __and__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__and__(other)  # type: ignore
-
-  def __xor__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__xor__(other)  # type: ignore
-
-  def __or__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__or__(other)  # type: ignore
-
-  def __radd__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__radd__(other)  # type: ignore
-
-  def __rsub__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rsub__(other)  # type: ignore
-
-  def __rmul__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rmul__(other)  # type: ignore
-
-  def __rmatmul__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rmatmul__(other)  # type: ignore
-
-  def __rtruediv__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rtruediv__(other)  # type: ignore
-
-  def __rfloordiv__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rfloordiv__(other)  # type: ignore
-
-  def __rmod__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rmod__(other)  # type: ignore
-
-  def __rdivmod__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rdivmod__(other)  # type: ignore
-
-  def __rpow__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rpow__(other)  # type: ignore
-
-  def __rlshift__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rlshift__(other)  # type: ignore
-
-  def __rrshift__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rrshift__(other)  # type: ignore
-
-  def __rand__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rand__(other)  # type: ignore
-
-  def __rxor__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__rxor__(other)  # type: ignore
-
-  def __ror__(self, other) -> A:
-    if isinstance(other, Variable):
-      other = other.value
-    return self.value.__ror__(other)  # type: ignore
-
+    return item in self.get_value()  # type: ignore
+
+  # binary operators
+  __add__ = _variable_operator('__add__')
+  __sub__ = _variable_operator('__sub__')
+  __mul__ = _variable_operator('__mul__')
+  __matmul__ = _variable_operator('__matmul__')
+  __truediv__ = _variable_operator('__truediv__')
+  __floordiv__ = _variable_operator('__floordiv__')
+  __mod__ = _variable_operator('__mod__')
+  __pow__ = _variable_operator('__pow__')
+  __lshift__ = _variable_operator('__lshift__')
+  __rshift__ = _variable_operator('__rshift__')
+  __and__ = _variable_operator('__and__')
+  __xor__ = _variable_operator('__xor__')
+  __or__ = _variable_operator('__or__')
+  __radd__ = _variable_operator('__radd__')
+  __rsub__ = _variable_operator('__rsub__')
+  __rmul__ = _variable_operator('__rmul__')
+  __rmatmul__ = _variable_operator('__rmatmul__')
+  __rtruediv__ = _variable_operator('__rtruediv__')
+  __rfloordiv__ = _variable_operator('__rfloordiv__')
+  __rmod__ = _variable_operator('__rmod__')
+  __rpow__ = _variable_operator('__rpow__')
+  __rlshift__ = _variable_operator('__rlshift__')
+  __rrshift__ = _variable_operator('__rrshift__')
+  __rand__ = _variable_operator('__rand__')
+  __rxor__ = _variable_operator('__rxor__')
+  __ror__ = _variable_operator('__ror__')
+
+  # in-place operators
   def __iadd__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value += x` instead.'
+      'Use `variable[...] += x` instead.'
     )
 
   def __isub__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value -= x` instead.'
+      'Use `variable[...] -= x` instead.'
     )
 
   def __imul__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value *= x` instead.'
+      'Use `variable[...] *= x` instead.'
     )
 
   def __imatmul__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value @= x` instead.'
+      'Use `variable[...] @= x` instead.'
     )
 
   def __itruediv__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value /= x` instead.'
+      'Use `variable[...] /= x` instead.'
     )
 
   def __ifloordiv__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value //= x`` instead.'
+      'Use `variable[...] //= x` instead.'
     )
 
   def __imod__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value %= x` instead.'
+      'Use `variable[...] %= x` instead.'
     )
 
   def __ipow__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value **= x`` instead.'
+      'Use `variable[...] **= x` instead.'
     )
 
   def __ilshift__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value <<= x`` instead.'
+      'Use `variable[...] <<= x` instead.'
     )
 
   def __irshift__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value >>= x`` instead.'
+      'Use `variable[...] >>= x` instead.'
     )
 
   def __iand__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value &= x` instead.'
+      'Use `variable[...] &= x` instead.'
     )
 
   def __ixor__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value ^= x` instead.'
+      'Use `variable[...] ^= x` instead.'
     )
 
   def __ior__(self: V, other) -> V:
     raise NotImplementedError(
       'In-place operations are no longer supported for Variable.\n'
-      'Use `variable.value |= x` instead.'
+      'Use `variable[...] |= x` instead.'
     )
 
-  def __neg__(self) -> A:
-    return self.value.__neg__()  # type: ignore
-
-  def __pos__(self) -> A:
-    return self.value.__pos__()  # type: ignore
-
-  def __abs__(self) -> A:
-    return self.value.__abs__()  # type: ignore
-
-  def __invert__(self) -> A:
-    return self.value.__invert__()  # type: ignore
-
-  def __complex__(self) -> A:
-    return self.value.__complex__()  # type: ignore
-
-  def __int__(self) -> A:
-    return self.value.__int__()  # type: ignore
-
-  def __float__(self) -> A:
-    return self.value.__float__()  # type: ignore
-
-  def __index__(self) -> A:
-    return self.value.__index__()  # type: ignore
-
-  def __round__(self, ndigits: int) -> A:
-    return self.value.__round__(ndigits)  # type: ignore
-
-  def __trunc__(self) -> A:
-    return self.value.__trunc__()  # type: ignore
-
-  def __floor__(self) -> A:
-    return self.value.__floor__()  # type: ignore
-
-  def __ceil__(self) -> A:
-    return self.value.__ceil__()  # type: ignore
+  __neg__ = _variable_unary_operator('__neg__')
+  __pos__ = _variable_unary_operator('__pos__')
+  __abs__ = _variable_unary_operator('__abs__')
+  __invert__ = _variable_unary_operator('__invert__')
+  __complex__ = _variable_unary_operator('__complex__')
+  __int__ = _variable_unary_operator('__int__')
+  __float__ = _variable_unary_operator('__float__')
+  __index__ = _variable_unary_operator('__index__')
+  __trunc__ = _variable_unary_operator('__trunc__')
+  __floor__ = _variable_unary_operator('__floor__')
+  __ceil__ = _variable_unary_operator('__ceil__')
+
+  def __round__(self, ndigits: int = 0) -> A:
+    return self.get_value().__round__(ndigits)  # type: ignore
 
   # --------------------------------------------
 
   def __init_subclass__(cls) -> None:
+    if '__slots__' not in vars(cls):
+      cls.__slots__ = ()  # type: ignore[assignment]
     super().__init_subclass__()
-
     jax.tree_util.register_pytree_with_keys(
       cls,
       flatten_with_keys=_variable_flatten_with_keys,
@@ -906,13 +920,13 @@ def __init_subclass__(cls) -> None:
 
 def _variable_flatten_with_keys(x: Variable[tp.Any]):
   metadata = tuple(sorted(x._var_metadata.items()))
-  node = (jtu.GetAttrKey('value'), x.raw_value)
+  node = (jtu.GetAttrKey('value'), x._raw_value)
   return (node,), metadata
 
 
 def _variable_flatten(x: Variable[tp.Any]):
   metadata = tuple(sorted(x._var_metadata.items()))
-  return (x.raw_value,), metadata
+  return (x._raw_value,), metadata
 
 
 def _variable_unflatten(
@@ -920,7 +934,7 @@ def _variable_unflatten(
   static: tuple[tuple[str, tp.Any], ...],
   children: tuple[tp.Any],
 ):
-  return cls.from_metadata(value=children[0], attributes=dict(static))
+  return cls._new(children[0], dict(static))
 
 
 jax.tree_util.register_pytree_with_keys(
@@ -930,9 +944,9 @@ def _variable_unflatten(
   flatten_func=_variable_flatten,
 )
 
-
 VariableState = Variable
 
+
 class Param(Variable[A]):
   """The canonical learnable parameter. All learnable parameters
   in NNX layer modules will have the ``Param`` :class:`Variable`
@@ -1153,32 +1167,6 @@ def wrapper(*args):
   return wrapper  # type: ignore
 
 
-def split_flat_state(
-    flat_state: tp.Iterable[tuple[PathParts, Variable]],
-    filters: tuple[filterlib.Filter, ...],
-) -> tuple[list[tuple[PathParts, Variable]], ...]:
-  predicates = filterlib.filters_to_predicates(filters)
-  # we have n + 1 states, where n is the number of predicates
-  # the last state is for values that don't match any predicate
-  flat_states: tuple[list[tuple[PathParts, Variable]], ...] = (
-    tuple([] for _ in predicates)
-  )
-
-  for path, value in flat_state:
-    for i, predicate in enumerate(predicates):
-      if predicate(path, value):
-        flat_states[i].append((path, value))
-        break
-    else:
-      raise ValueError(
-        'Non-exhaustive filters, got a non-empty remainder: '
-        f'{path} -> {value}.'
-        '\nUse `...` to match all remaining elements.'
-      )
-
-  return flat_states
-
-
 ###################################################
 ### Variable type/class <-> string name mapping ###
 ###################################################
@@ -1232,13 +1220,6 @@ def variable_name_from_type(
   return name
 
 
-class _Missing:
-  pass
-
-
-_MISSING = _Missing()
-
-
 @tp.overload
 def register_variable_name(
   name: str,
@@ -1258,12 +1239,12 @@ def register_variable_name(
 
 def register_variable_name(
   name: str,
-  typ: type[Variable[A]] | _Missing = _MISSING,
+  typ: type[Variable[A]] | Missing = MISSING,
   *,
   overwrite=False,
 ) -> type[Variable[A]] | tp.Callable[[type[Variable[A]]], type[Variable[A]]]:
   """Register a pair of Linen collection name and its NNX type."""
-  if typ is _MISSING:
+  if isinstance(typ, Missing):
     return partial(register_variable_name, name, overwrite=overwrite)
   typ = tp.cast(type[Variable[A]], typ)
   if not overwrite and name in VariableTypeCache:
diff --git a/tests/nnx/containers_test.py b/tests/nnx/containers_test.py
index 70b51283f..7aa80cb73 100644
--- a/tests/nnx/containers_test.py
+++ b/tests/nnx/containers_test.py
@@ -34,7 +34,7 @@ def test_on_set_value(self):
     )
     x[...] = 5
 
-    assert x.raw_value == 12
+    assert x.get_raw_value() == 12
 
   def test_module_unbox(self):
     class Foo(nnx.Module):
@@ -43,8 +43,8 @@ def __init__(self) -> None:
 
     module = Foo()
 
-    assert module.x.value == 4
-    assert vars(module)['x'].raw_value == 1
+    assert module.x.get_value() == 4
+    assert vars(module)['x'].get_raw_value() == 1
 
   def test_module_box(self):
     class Foo(nnx.Module):
@@ -58,7 +58,7 @@ def __init__(self) -> None:
     module.x[...] = 5
 
     assert module.x[...] == 12
-    assert vars(module)['x'].raw_value == 12
+    assert vars(module)['x'][...] == 12
 
 
 if __name__ == '__main__':
diff --git a/tests/nnx/graph_utils_test.py b/tests/nnx/graph_utils_test.py
index 523ff31fa..30638b3b8 100644
--- a/tests/nnx/graph_utils_test.py
+++ b/tests/nnx/graph_utils_test.py
@@ -46,8 +46,8 @@ def test_flatten(self):
     refmap = nnx.graph.RefMap()
     graphdef, flat_state = nnx.graph.flatten(g, ref_index=refmap)
 
-    assert flat_state[0][1].value == 2
-    assert flat_state[1][1].value == 4
+    assert flat_state[0][1].get_value() == 2
+    assert flat_state[1][1].get_value() == 4
 
     assert len(refmap) == 2  # 2 Variables
     assert a['b'] in refmap
@@ -156,8 +156,8 @@ def test_update_dynamic(self):
     state[0]['b'][...] = 3
     nnx.update(g, state)
 
-    assert g[0]['b'].value == 3
-    assert g[2]['b'].value == 3
+    assert g[0]['b'][...] == 3
+    assert g[2]['b'][...] == 3
 
   def test_update_from_pure_dict(self):
     a = {'a': 1, 'b': nnx.Param(jnp.array(2))}
@@ -342,7 +342,7 @@ def __init__(self):
     m2 = nnx.merge(graphdef, state)
 
     assert isinstance(m2.tree, Tree)
-    assert m2.tree.a.raw_value == 1
+    assert m2.tree.a.get_value() == 1
     assert m2.tree.b == 'a'
     assert m2.tree.a is m.tree.a
     assert m2.tree is not m.tree
diff --git a/tests/nnx/integration_test.py b/tests/nnx/integration_test.py
index 67eaa71c5..e70919683 100644
--- a/tests/nnx/integration_test.py
+++ b/tests/nnx/integration_test.py
@@ -146,17 +146,17 @@ def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
         key = rngs.params()
         self.w = nnx.Param(jax.random.uniform(key, (din, dout)))
         self.b = nnx.Param(jnp.zeros((dout,)))
-        self.count = State(0)
+        self.count = State(jnp.array(0))
 
       def __call__(self, x):
-        self.count.value += 1
-        return x @ self.w.value + self.b.value[None]
+        self.count[...] += 1
+        return x @ self.w + self.b[None]
 
     model = Linear(din=12, dout=2, rngs=nnx.Rngs(0))
     # forward pass
     x = jnp.ones((8, 12))
     y = model(x)
-    assert model.count.value == 1
+    assert model.count[...] == 1
 
     @nnx.jit
     def train_step(model, x, y):
@@ -176,7 +176,7 @@ def loss_fn(model):
 
     # execute the training step
     train_step(model, x, y)
-    assert model.count.value == 2
+    assert model.count[...] == 2
 
   def test_functional_example(self):
     class Count(nnx.Variable[A]):
@@ -187,17 +187,17 @@ def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
         key = rngs.params()
         self.w = nnx.Param(jax.random.uniform(key, (din, dout)))
         self.b = nnx.Param(jnp.zeros((dout,)))
-        self.count = Count(0)
+        self.count = Count(jnp.array(0))
 
       def __call__(self, x):
-        self.count.value += 1
-        return x @ self.w.value + self.b.value[None]
+        self.count[...] += 1
+        return x @ self.w + self.b[None]
 
     model = Linear(din=12, dout=2, rngs=nnx.Rngs(0))
     # forward pass
     x = jnp.ones((8, 12))
     y = model(x)
-    assert model.count.value == 1
+    assert model.count[...] == 1
 
     graphdef, params, counts = nnx.split(model, nnx.Param, Count)
 
@@ -218,7 +218,7 @@ def loss_fn(params):
     # execute the training step
     params, counts = train_step(params, counts, x, y)
     model = nnx.merge(graphdef, params, counts)
-    assert model.count.value == 2
+    assert model.count[...] == 2
 
   def test_intermediates_example(self):
     class Linear(nnx.Module):
@@ -228,7 +228,7 @@ def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
         self.b = nnx.Param(jnp.zeros((dout,)))
 
       def __call__(self, x):
-        y = x @ self.w.value + self.b.value[None]
+        y = x @ self.w + self.b[None]
         self.y = nnx.Intermediate(y)
         return y
 
@@ -248,7 +248,7 @@ def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
         self.b = nnx.Param(jnp.zeros((dout,)))
 
       def __call__(self, x):
-        y = x @ self.w.value + self.b.value[None]
+        y = x @ self.w + self.b[None]
         self.y = nnx.Intermediate(y)
         return y
 
@@ -310,7 +310,7 @@ def __call__(self, x):
         x = nnx.relu(self.dropout(self.bn(self.linear(x))))
         return self.linear_out(x)
 
-    with nnx.use_refs(True):
+    with nnx.use_hijax(True):
       model = Model(2, 64, 3, rngs=nnx.Rngs(0))  # eager initialization
       optimizer = nnx.Optimizer(model, optax.adam(1e-3), wrt=nnx.Param)
 
@@ -319,9 +319,9 @@ def train_step(x, y):
       graphdef, params, nondiff = nnx.split(model, nnx.Param, ...)
       def loss_fn(params):
         model =  nnx.merge(graphdef, params, nondiff)
-        return ((model(x) - y) ** 2).mean() # call methods directly
+        return ((model(x) - y) ** 2).mean()  # call methods directly
 
-      loss, grads = jax.value_and_grad(loss_fn)(nnx.to_arrays(params))
+      loss, grads = jax.value_and_grad(loss_fn)(nnx.to_lojax(params))
       optimizer.update(model, grads)  # in-place updates
 
       return loss
diff --git a/tests/nnx/module_test.py b/tests/nnx/module_test.py
index 64bf1dda2..a5e17bd7a 100644
--- a/tests/nnx/module_test.py
+++ b/tests/nnx/module_test.py
@@ -1,4 +1,5 @@
 # Copyright 2024 The Flax Authors.
+
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -333,13 +334,13 @@ def test_clone(self):
     m2 = nnx.clone(m)
 
     assert m is not m2
-    assert m2.a[0].value == m2.b.c.value
-    assert m2.a[1].value == m2.b.d.value
+    assert m2.a[0].get_value() == m2.b.c.get_value()
+    assert m2.a[1].get_value() == m2.b.d.get_value()
 
-    assert m.a[0].value == m2.a[0].value
-    assert m.a[1].value == m2.a[1].value
-    assert m.b.c.value == m2.b.c.value
-    assert m.b.d.value == m2.b.d.value
+    assert m.a[0].get_value() == m2.a[0].get_value()
+    assert m.a[1].get_value() == m2.a[1].get_value()
+    assert m.b.c.get_value() == m2.b.c.get_value()
+    assert m.b.d.get_value() == m2.b.d.get_value()
 
   def test_sow_basic(self):
     class Foo(nnx.Module):
@@ -354,12 +355,12 @@ def __call__(self, x):
 
     assert y1 == 3
     assert y2 == 11
-    assert m.y.value == (3, 11)
+    assert m.y.get_value() == (3, 11)
 
     intermediates = nnx.pop(m, nnx.Intermediate)
 
     assert isinstance(intermediates['y'], nnx.Intermediate)
-    assert intermediates['y'].value == (3, 11)
+    assert intermediates['y'].get_value() == (3, 11)
 
     assert not hasattr(m, 'y')
 
@@ -550,13 +551,13 @@ def add_submodule(self):
   def test_create_abstract(self):
     linear = nnx.eval_shape(lambda: nnx.Linear(2, 3, rngs=nnx.Rngs(0)))
 
-    assert linear.kernel.value == jax.ShapeDtypeStruct((2, 3), jnp.float32)
-    assert linear.bias.value == jax.ShapeDtypeStruct((3,), jnp.float32)
+    assert linear.kernel.get_value() == jax.ShapeDtypeStruct((2, 3), jnp.float32)
+    assert linear.bias.get_value() == jax.ShapeDtypeStruct((3,), jnp.float32)
 
   def test_create_abstract_stateful(self):
     linear = nnx.eval_shape(lambda: nnx.Dropout(0.5, rngs=nnx.Rngs(0)))
 
-    assert linear.rngs.key.value == jax.ShapeDtypeStruct(
+    assert linear.rngs.key.get_value() == jax.ShapeDtypeStruct(
       (), jax.random.key(0).dtype
     )
 
@@ -742,13 +743,13 @@ class Foo(nnx.Module):
     graphdef, state = nnx.split(m)
 
     assert len(state) == 4
-    assert state['b'].value == 2
+    assert state['b'].get_value() == 2
     assert isinstance(state['b'], nnx.Variable)
-    assert state['c'].value == 3
+    assert state['c'].get_value() == 3
     assert isinstance(state['c'], nnx.Param)
-    assert state['d'].value == 4
+    assert state['d'].get_value() == 4
     assert isinstance(state['d'], nnx.Variable)
-    assert state['e'].value == 5
+    assert state['e'].get_value() == 5
     assert isinstance(state['e'], nnx.BatchStat)
 
   def test_post_init(self):
diff --git a/tests/nnx/mutable_array_test.py b/tests/nnx/mutable_array_test.py
index d30a2489d..4339c2b8a 100644
--- a/tests/nnx/mutable_array_test.py
+++ b/tests/nnx/mutable_array_test.py
@@ -15,22 +15,22 @@
 import dataclasses
 from absl.testing import absltest
 import optax
+import pytest
 from flax import nnx
 import flax.errors
 import jax
 import jax.numpy as jnp
 
 
-
 class TestObject(absltest.TestCase):
   @classmethod
   def setUpClass(cls):
-    cls.using_refs = nnx.using_refs()
-    nnx.use_refs(True)
+    cls.using_hijax = nnx.current_variable_mode()
+    nnx.variable_mode('hijax')
 
   @classmethod
   def tearDownClass(cls):
-    nnx.use_refs(cls.using_refs)
+    nnx.variable_mode(cls.using_hijax)
 
   def test_pytree(self):
     class Foo(nnx.Module):
@@ -127,12 +127,12 @@ def __init__(self, a):
 class TestMutableArrayGraph(absltest.TestCase):
   @classmethod
   def setUpClass(cls):
-    cls.using_refs = nnx.using_refs()
-    nnx.use_refs(True)
+    cls.using_hijax = nnx.current_variable_mode()
+    nnx.variable_mode('hijax')
 
   @classmethod
   def tearDownClass(cls):
-    nnx.use_refs(cls.using_refs)
+    nnx.variable_mode(cls.using_hijax)
 
   def test_split_mutable_array(self):
     m = jax.new_ref(1)
@@ -150,14 +150,14 @@ def __init__(self):
         self.a = nnx.Param(1)
 
     m = Foo()
-    self.assertTrue(m.a.has_ref)
+    self.assertEqual(m.a.mode, 'hijax')
 
-    m2 = nnx.to_arrays(m)
-    self.assertFalse(m2.a.has_ref)
+    m2 = nnx.to_lojax(m)
+    self.assertEqual(m2.a.mode, 'lojax')
     self.assertIsNot(m, m2)
 
-    m3 = nnx.to_refs(m2)
-    self.assertTrue(m3.a.has_ref)
+    m3 = nnx.to_hijax(m2)
+    self.assertEqual(m3.a.mode, 'hijax')
     self.assertIsNot(m2, m3)
     self.assertIsNot(m2.a, m3.a)
 
@@ -188,17 +188,17 @@ def __init__(self):
         self.b = nnx.BatchStat(2)
 
     m = Foo()
-    self.assertTrue(m.a.has_ref)
-    self.assertTrue(m.b.has_ref)
+    self.assertEqual(m.a.mode, 'hijax')
+    self.assertEqual(m.b.mode, 'hijax')
 
-    m2 = nnx.to_arrays(m, only=nnx.BatchStat)
-    self.assertTrue(m2.a.has_ref)
-    self.assertFalse(m2.b.has_ref)
+    m2 = nnx.to_lojax(m, only=nnx.BatchStat)
+    self.assertEqual(m2.a.mode, 'hijax')
+    self.assertEqual(m2.b.mode, 'lojax')
     self.assertIsNot(m, m2)
 
-    m3 = nnx.to_refs(m2, nnx.BatchStat)
-    self.assertTrue(m3.a.has_ref)
-    self.assertTrue(m3.b.has_ref)
+    m3 = nnx.to_hijax(m2, only=nnx.BatchStat)
+    self.assertEqual(m3.a.mode, 'hijax')
+    self.assertEqual(m3.b.mode, 'hijax')
     self.assertIsNot(m2, m3)
     self.assertIs(m.a, m3.a)
 
@@ -233,7 +233,7 @@ def __init__(self):
   def test_mutable_array_split_merge_in_variable(self):
     class Foo(nnx.Module):
       def __init__(self):
-        self.a = nnx.Param(1, use_ref=True)
+        self.a = nnx.Param(1, mode='hijax')
         self.b = self.a
 
     m = Foo()
@@ -241,7 +241,7 @@ def __init__(self):
     ref_map = nnx.graph.RefMap()
     graphdef, state = nnx.graph.flatten(m, ref_index=ref_map)
     self.assertLen(state, 1)
-    self.assertLen(ref_map, 3)  # 1 Foo + 1 Param + 1 ArrayRef
+    self.assertLen(ref_map, 2)  # 1 Foo + 1 Param
 
     m1 = nnx.merge(graphdef, state)
     self.assertIs(m1.a, m1.b)
@@ -251,20 +251,18 @@ def test_mutable_array_split_merge_in_variable_shared_array(self):
     class Foo(nnx.Module):
       def __init__(self):
         m_array = 1
-        self.a = nnx.Param(m_array, use_ref=True)
-        self.b = nnx.Param(m_array, use_ref=True)
+        self.a = nnx.Param(m_array, mode='hijax')
+        self.b = nnx.Param(m_array, mode='hijax')
 
     m = Foo()
-    self.assertIsNot(m.a.raw_value, m.b.raw_value)
 
     ref_map = nnx.graph.RefMap()
     graphdef, state = nnx.graph.flatten(m, ref_index=ref_map)
     self.assertLen(state, 2)
-    self.assertLen(ref_map, 5)  # 1 Foo + 2 Param + 2 ArrayRefs
+    self.assertLen(ref_map, 3)  # 1 Foo + 2 Param
 
     m1 = nnx.merge(graphdef, state)
     # Each variable will own its own array and ref.
-    self.assertIsNot(m1.a.raw_value, m1.b.raw_value)
     self.assertIsInstance(m1.a, nnx.Param)
 
   def test_mutable_example(self):
@@ -283,15 +281,20 @@ def __init__(self):
 
     ref_map = nnx.graph.RefMap()
     graphdef, state = nnx.graph.flatten(m, ref_index=ref_map)
-    state = nnx.to_arrays(state)
+    state = nnx.to_lojax(state)
     self.assertLen(state, 1)
 
-    m1 = nnx.merge(graphdef, nnx.to_refs(state))
+    m1 = nnx.merge(graphdef, nnx.to_hijax(state))
     self.assertIs(m1.a, m1.b)
     self.assertIsInstance(m1.a, jax.Ref)
 
   def test_update_context(self):
-    m1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+    class Foo(nnx.Module):
+      def __init__(self):
+        self.kernel = jax.new_ref(1)
+        self.bias = jax.new_ref(2)
+
+    m1 = Foo()
     with nnx.update_context('example'):
       with nnx.split_context('example') as ctx:
         graphdef, state = ctx.split(m1)
@@ -299,19 +302,15 @@ def test_update_context(self):
       with nnx.merge_context('example', True) as ctx:
         m2 = ctx.merge(graphdef, state)
 
-      m_out1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+      m_out1 = Foo()
 
       with nnx.split_context('example') as ctx:
         graphdef_out, state_out = ctx.split((m2, m_out1, m2))
 
-      self.assertIsInstance(state_out[0]['kernel'].value, nnx.graph.NoUpdate)
-      self.assertIsInstance(state_out[0]['bias'].value, nnx.graph.NoUpdate)
-      self.assertIsInstance(
-        state_out[1]['kernel'].value, nnx.graph.ArrayRefOutput
-      )
-      self.assertIsInstance(
-        state_out[1]['bias'].value, nnx.graph.ArrayRefOutput
-      )
+      self.assertIsInstance(state_out[0]['kernel'], nnx.graph.NoUpdate)
+      self.assertIsInstance(state_out[0]['bias'], nnx.graph.NoUpdate)
+      self.assertIsInstance(state_out[1]['kernel'], nnx.graph.ArrayRefOutput)
+      self.assertIsInstance(state_out[1]['bias'], nnx.graph.ArrayRefOutput)
       # 2 ArrayRefOutput + 2 NoUpdate, however, NoUpdate are empty nodes
       self.assertLen(jax.tree.leaves(state_out), 2)
 
@@ -322,7 +321,12 @@ def test_update_context(self):
       self.assertIsNot(m_out2, m_out1)
 
   def test_update_context_flatten(self):
-    m1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+    class Foo(nnx.Module):
+      def __init__(self):
+        self.kernel = jax.new_ref(1)
+        self.bias = jax.new_ref(2)
+
+    m1 = Foo()
     with nnx.update_context('example'):
       with nnx.split_context('example') as ctx:
         graphdef, state = ctx.flatten(m1)
@@ -330,24 +334,20 @@ def test_update_context_flatten(self):
       with nnx.merge_context('example', True) as ctx:
         m2 = ctx.merge(graphdef, state)
 
-      m_out1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+      m_out1 = Foo()
 
       with nnx.split_context('example') as ctx:
         graphdef_out, state_out = ctx.flatten((m2, m_out1, m2))
 
       state_out_dict = dict(state_out)
 
+      self.assertIsInstance(state_out_dict[(0, 'kernel')], nnx.graph.NoUpdate)
+      self.assertIsInstance(state_out_dict[(0, 'bias')], nnx.graph.NoUpdate)
       self.assertIsInstance(
-        state_out_dict[(0, 'kernel')].value, nnx.graph.NoUpdate
-      )
-      self.assertIsInstance(
-        state_out_dict[(0, 'bias')].value, nnx.graph.NoUpdate
-      )
-      self.assertIsInstance(
-        state_out_dict[(1, 'kernel')].value, nnx.graph.ArrayRefOutput
+        state_out_dict[(1, 'kernel')], nnx.graph.ArrayRefOutput
       )
       self.assertIsInstance(
-        state_out_dict[(1, 'bias')].value, nnx.graph.ArrayRefOutput
+        state_out_dict[(1, 'bias')], nnx.graph.ArrayRefOutput
       )
       # 2 ArrayRefOutput + 2 NoUpdate, however, NoUpdate are empty nodes
       self.assertLen(jax.tree.leaves(state_out), 2)
@@ -359,29 +359,34 @@ def test_update_context_flatten(self):
       self.assertIsNot(m_out2, m_out1)
 
   def test_update_context_to_tree1(self):
-    m1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+    class Foo(nnx.Module):
+      def __init__(self):
+        self.kernel = jax.new_ref(1)
+        self.bias = jax.new_ref(2)
+
+    m1 = Foo()
     with nnx.update_context('example'):
       m1_tree = nnx.to_tree((m1,), ctxtag='example')
 
       (m2,) = nnx.from_tree(m1_tree, ctxtag='example', is_inner=True)
 
-      m_out1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+      m_out1 = Foo()
 
       # with nnx.split_context('example') as ctx:
       #   graphdef_out, state_out = ctx.split((m2, m_out1))
       out_tree = nnx.to_tree(((m2,), m_out1, m2), ctxtag='example')
 
       self.assertIsInstance(
-        out_tree[0][0].states[0]['kernel'].value, nnx.graph.NoUpdate
+        out_tree[0][0].states[0]['kernel'], nnx.graph.NoUpdate
       )
       self.assertIsInstance(
-        out_tree[0][0].states[0]['bias'].value, nnx.graph.NoUpdate
+        out_tree[0][0].states[0]['bias'], nnx.graph.NoUpdate
       )
       self.assertIsInstance(
-        out_tree[1].states[0]['kernel'].value, nnx.graph.ArrayRefOutput
+        out_tree[1].states[0]['kernel'], nnx.graph.ArrayRefOutput
       )
       self.assertIsInstance(
-        out_tree[1].states[0]['bias'].value, nnx.graph.ArrayRefOutput
+        out_tree[1].states[0]['bias'], nnx.graph.ArrayRefOutput
       )
       self.assertEmpty(out_tree[2].states[0])  # Repeated m2 State
 
@@ -398,29 +403,34 @@ def test_update_context_to_tree1(self):
       self.assertIsNot(m_out2, m_out1)
 
   def test_update_context_to_tree2(self):
-    m1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+    class Foo(nnx.Module):
+      def __init__(self):
+        self.kernel = jax.new_ref(1)
+        self.bias = jax.new_ref(2)
+
+    m1 = Foo()
     with nnx.update_context('example') as ctx:
       m1_tree = nnx.to_tree((m1,), ctxtag='example')
 
       (m2,) = nnx.from_tree(m1_tree, ctxtag='example', is_inner=True)
 
-      m_out1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+      m_out1 = Foo()
 
       # with nnx.split_context('example') as ctx:
       #   graphdef_out, state_out = ctx.split((m2, m_out1))
       out_tree = nnx.to_tree(((m2,), m_out1, m2), ctxtag='example')
 
       self.assertIsInstance(
-        out_tree[0][0].states[0]['kernel'].value, nnx.graph.NoUpdate
+        out_tree[0][0].states[0]['kernel'], nnx.graph.NoUpdate
       )
       self.assertIsInstance(
-        out_tree[0][0].states[0]['bias'].value, nnx.graph.NoUpdate
+        out_tree[0][0].states[0]['bias'], nnx.graph.NoUpdate
       )
       self.assertIsInstance(
-        out_tree[1].states[0]['kernel'].value, nnx.graph.ArrayRefOutput
+        out_tree[1].states[0]['kernel'], nnx.graph.ArrayRefOutput
       )
       self.assertIsInstance(
-        out_tree[1].states[0]['bias'].value, nnx.graph.ArrayRefOutput
+        out_tree[1].states[0]['bias'], nnx.graph.ArrayRefOutput
       )
       self.assertEmpty(out_tree[2].states[0])  # Repeated m2 State
 
@@ -437,29 +447,34 @@ def test_update_context_to_tree2(self):
       self.assertIsNot(m_out2, m_out1)
 
   def test_update_context_to_tree_trivial_prefix(self):
-    m1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+    class Foo(nnx.Module):
+      def __init__(self):
+        self.kernel = jax.new_ref(1)
+        self.bias = jax.new_ref(2)
+
+    m1 = Foo()
     with nnx.update_context('example'):
       m1_tree = nnx.to_tree((m1,), ctxtag='example', prefix=0)
 
       (m2,) = nnx.from_tree(m1_tree, ctxtag='example', is_inner=True, prefix=0)
 
-      m_out1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
+      m_out1 = Foo()
 
       # with nnx.split_context('example') as ctx:
       #   graphdef_out, state_out = ctx.split((m2, m_out1))
       out_tree = nnx.to_tree(((m2,), m_out1, m2), ctxtag='example', prefix=0)
 
       self.assertIsInstance(
-        out_tree[0][0].states[0]['kernel'].value, nnx.graph.NoUpdate
+        out_tree[0][0].states[0]['kernel'], nnx.graph.NoUpdate
       )
       self.assertIsInstance(
-        out_tree[0][0].states[0]['bias'].value, nnx.graph.NoUpdate
+        out_tree[0][0].states[0]['bias'], nnx.graph.NoUpdate
       )
       self.assertIsInstance(
-        out_tree[1].states[0]['kernel'].value, nnx.graph.ArrayRefOutput
+        out_tree[1].states[0]['kernel'], nnx.graph.ArrayRefOutput
       )
       self.assertIsInstance(
-        out_tree[1].states[0]['bias'].value, nnx.graph.ArrayRefOutput
+        out_tree[1].states[0]['bias'], nnx.graph.ArrayRefOutput
       )
       self.assertEmpty(out_tree[2].states[0])  # Repeated m2 State
 
@@ -480,12 +495,12 @@ def test_update_context_to_tree_trivial_prefix(self):
 class TestMutableArrayNNXTransforms(absltest.TestCase):
   @classmethod
   def setUpClass(cls):
-    cls.using_refs = nnx.using_refs()
-    nnx.use_refs(True)
+    cls.using_hijax = nnx.current_variable_mode()
+    nnx.variable_mode('hijax')
 
   @classmethod
   def tearDownClass(cls):
-    nnx.use_refs(cls.using_refs)
+    nnx.variable_mode(cls.using_hijax)
 
   def test_simple_jit(self):
     m1 = nnx.Linear(1, 1, rngs=nnx.Rngs(0))
@@ -501,7 +516,7 @@ def f(m2):
 
     self.assertIsNot(m_out1, m_out2)
     self.assertIsInstance(m_out2.kernel, nnx.Param)
-    self.assertIsInstance(m_out2.kernel.raw_value, jax.Ref)
+    self.assertIsInstance(m_out2.kernel[...], jax.Array)
 
   def test_jit_mutable(self):
     @dataclasses.dataclass
@@ -525,12 +540,12 @@ def f(m2: Foo):
 class TestMutableArray(absltest.TestCase):
   @classmethod
   def setUpClass(cls):
-    cls.using_refs = nnx.using_refs()
-    nnx.use_refs(True)
+    cls.using_hijax = nnx.current_variable_mode()
+    nnx.variable_mode('hijax')
 
   @classmethod
   def tearDownClass(cls):
-    nnx.use_refs(cls.using_refs)
+    nnx.variable_mode(cls.using_hijax)
 
   def test_static(self):
     class C(nnx.Module):
@@ -554,12 +569,12 @@ def f(x):
     assert n == 2
 
   def test_variable_creation(self):
-    v = nnx.Variable(1)
+    v = nnx.Variable(jnp.array(1))
     self.assertEqual(v[...], 1)
-    self.assertTrue(v.has_ref)
+    self.assertEqual(v.mode, 'hijax')
 
   def test_variable_metadata(self):
-    v = nnx.Variable(1, a=2, b=3)
+    v = nnx.Variable(jnp.array(1), a=2, b=3)
     self.assertEqual(v.a, 2)
     self.assertEqual(v.b, 3)
 
@@ -568,7 +583,7 @@ class Params(nnx.Pytree):
       def __init__(self, din: int, dout: int):
         self.w = nnx.Param(jnp.zeros((din, dout), jnp.float32))
         self.b = nnx.Param(jnp.zeros((dout,), jnp.float32))
-        self.count = nnx.Variable(0)
+        self.count = nnx.Variable(jnp.array(0))
 
     params: Params
     params = Params(3, 4)
@@ -580,18 +595,9 @@ def __init__(self, din: int, dout: int):
     self.assertEqual(leaves[0].shape, (4,))  # b
     self.assertEqual(leaves[1].shape, ())  # count
     self.assertEqual(leaves[2].shape, (3, 4))  # w
-    self.assertEqual(
-      paths[0],
-      (jax.tree_util.GetAttrKey('b'), jax.tree_util.GetAttrKey('value')),
-    )
-    self.assertEqual(
-      paths[1],
-      (jax.tree_util.GetAttrKey('count'), jax.tree_util.GetAttrKey('value')),
-    )
-    self.assertEqual(
-      paths[2],
-      (jax.tree_util.GetAttrKey('w'), jax.tree_util.GetAttrKey('value')),
-    )
+    self.assertEqual(paths[0], (jax.tree_util.GetAttrKey('b'),))
+    self.assertEqual(paths[1], (jax.tree_util.GetAttrKey('count'),))
+    self.assertEqual(paths[2], (jax.tree_util.GetAttrKey('w'),))
 
     params = jax.tree.unflatten(treedef, leaves)
 
@@ -655,7 +661,6 @@ def test_rngs_create(self):
       (
         jax.tree_util.GetAttrKey('default'),
         jax.tree_util.GetAttrKey('count'),
-        jax.tree_util.GetAttrKey('value'),
       ),
     )
     self.assertEqual(
@@ -663,7 +668,6 @@ def test_rngs_create(self):
       (
         jax.tree_util.GetAttrKey('default'),
         jax.tree_util.GetAttrKey('key'),
-        jax.tree_util.GetAttrKey('value'),
       ),
     )
 
@@ -687,7 +691,7 @@ def __call__(self, x):
     x = jax.random.normal(jax.random.key(0), (5, 2))
     y = jnp.ones((5, 4))
 
-    with nnx.use_refs(False):
+    with nnx.variable_mode('lojax'):
       wrt = lambda path, x: path[-1] == 'w'
       model = Model(nnx.Rngs(1))
       optimizer = nnx.Optimizer(
@@ -717,8 +721,8 @@ def loss_fn(params):
   def test_optimize_mutable_arrays(self):
     class Model(nnx.Module):
       def __init__(self, rngs):
-        self.w = jax.new_ref(jax.random.uniform(rngs(), (2, 4)))
-        self.count = jax.new_ref(jnp.array(0))
+        self.w = nnx.Variable(jax.random.uniform(rngs(), (2, 4)))
+        self.count = nnx.Variable(jnp.array(0))
 
       def __call__(self, x):
         self.count[...] += 1
@@ -727,7 +731,7 @@ def __call__(self, x):
     x = jax.random.normal(jax.random.key(0), (5, 2))
     y = jnp.ones((5, 4))
 
-    with nnx.use_refs(True):
+    with nnx.variable_mode('hijax'):
       wrt = lambda path, x: path[-1] == 'w'
       model = Model(nnx.Rngs(1))
       optimizer = nnx.Optimizer(model, tx=optax.adam(1e-3), wrt=wrt)
@@ -740,7 +744,7 @@ def loss_fn(params):
         model = nnx.merge(graphdef, params, nondiff)
         return jnp.mean((model(x) - y) ** 2)
 
-      loss, grads = jax.value_and_grad(loss_fn)(nnx.to_arrays(params))
+      loss, grads = jax.value_and_grad(loss_fn)(nnx.to_lojax(params))
       optimizer.update(params, grads)
       return loss
 
@@ -748,6 +752,158 @@ def loss_fn(params):
 
     self.assertNotEqual(loss, 0.0)
 
+class TestHijaxVariables(absltest.TestCase):
+  def test_variable_to_hijax(self):
+    v_low = nnx.Param(jnp.array(1), a='hi')
+    v_hi = nnx.to_hijax(v_low)
+
+    self.assertEqual(v_hi.mode, 'hijax')
+    self.assertEqual(v_hi[...], 1)
+    self.assertIsInstance(v_hi, nnx.Param)
+
+    v_hi[...] = 2
+    self.assertEqual(v_hi[...], 2)
+
+    @jax.jit
+    def set(v_hi: nnx.Param, a):
+      self.assertIsInstance(v_hi, nnx.Param)
+      v_hi[...] = a
+      self.assertEqual(v_hi.a, 'hi')
+      self.assertEqual(v_hi.mode, 'hijax')
+      v_hi[...] += 5
+      return v_hi + 2
+
+    y = set(v_hi, 10)
+    self.assertEqual(v_hi[...], 15)
+    self.assertEqual(y, 17)
+
+    v_low = nnx.to_lojax(v_hi)
+    self.assertEqual(v_low.mode, 'lojax')
+    self.assertIsInstance(v_low, nnx.Param)
+
+  def test_from_metadata(self):
+    value = 1
+    metadata = {'a': 'hi', 'mode': 'lojax'}
+    v_low = nnx.Param.from_metadata(value, metadata)
+    self.assertIsInstance(v_low, nnx.Param)
+    self.assertEqual(v_low.mode, 'lojax')
+
+    metadata['mode'] = 'hijax'
+    v_hi = nnx.Param.from_metadata(value, metadata)
+    self.assertIsInstance(v_hi, nnx.Param)
+    self.assertEqual(v_hi.mode, 'hijax')
+
+  def test_variable_to_hijax_clean(self):
+    v_low = nnx.Param(jnp.array([1]), tag='hello')
+    print()
+    print(v_low)
+    assert v_low.mode == 'lojax'
+    v_hi = nnx.to_hijax(v_low)
+    v_hi[...] = jnp.array([2])
+    assert v_hi.mode == 'hijax'
+    print(v_hi)
+    assert v_hi[...] == 2
+
+    @jax.jit
+    def set(v_hi, a):
+      v_hi[...] = a
+      print(v_hi)
+      assert v_hi.tag == 'hello'
+
+    set(v_hi, 10)
+
+    assert v_hi[...] == 10
+
+    v_low = nnx.to_lojax(v_hi)
+
+    assert v_low.mode == 'lojax'
+    assert v_low[...] == 10
+
+  def test_hijax_and_pytree(self):
+    class Foo(nnx.Pytree):
+      def __init__(self, din, dout, rngs: nnx.Rngs):
+        self.w = nnx.Param(rngs.uniform((din, dout)))
+        self.b = nnx.Param(jnp.zeros((dout,)))
+        self.count = nnx.Variable(0)
+
+    foo = Foo(2, 4, nnx.Rngs(1))
+    assert foo.w.mode == 'lojax'
+    assert foo.b.mode == 'lojax'
+
+    foo = nnx.to_hijax(foo)
+
+    assert foo.w.mode == 'hijax'
+    assert foo.b.mode == 'hijax'
+
+    @jax.jit
+    def forward(foo, x):
+      foo.count[...] += 1
+      return x @ foo.w + foo.b[None]
+
+    x = jnp.ones((1, 2))
+    y = forward(foo, x)
+    assert y.shape == (1, 4)
+    assert foo.count[...] == 1
+
+  def test_use_hijax(self):
+    v_low = nnx.Param(1, a='hi')
+    self.assertEqual(v_low.mode, 'lojax')
+
+    v_hi = nnx.Param(1, a='hi', mode='hijax')
+    self.assertEqual(v_hi.mode, 'hijax')
+
+    with nnx.variable_mode('hijax'):
+      v2 = nnx.Param(1, a='hi')
+      self.assertEqual(v2.mode, 'hijax')
+
+  @nnx.variable_mode('hijax')
+  def test_hijax_rngs(self):
+    rngs = nnx.Rngs(0)
+
+    @jax.jit
+    def f(rngs: nnx.Rngs):
+      return rngs()
+
+    k1 = f(rngs)
+    k2 = f(rngs)
+
+    assert k1 != k2
+
+  @pytest.mark.skip(reason='not yet supported')
+  def test_return_hijax_from_transform(self):
+    @jax.jit
+    def create_var():
+      return nnx.Param(1, mode='hijax')
+
+    v = create_var()
+    self.assertEqual(v.mode, 'hijax')
+
+  @pytest.mark.skip(reason='not yet supported')
+  @nnx.variable_mode('hijax')
+  def test_lower(self):
+    v = nnx.Param(jnp.ones((2, 3)))
+
+    @jax.jit
+    def f(v):
+      v[...] += 1
+      return v[...]
+
+    e = f.lower(v)
+    y = e.out_info[2]
+    self.assertEqual(y.shape, ())
+
+  @nnx.variable_mode('hijax')
+  def test_eval_shape(self):
+    v = nnx.Param(jnp.array(0))
+
+    def f(v):
+      v[...] += 1
+      return v[...]
+
+    y = jax.eval_shape(f, v)
+
+    self.assertEqual(y.shape, ())
+
 
 if __name__ == '__main__':
   absltest.main()
diff --git a/tests/nnx/nn/embed_test.py b/tests/nnx/nn/embed_test.py
index 8991c8a35..3120ad84d 100644
--- a/tests/nnx/nn/embed_test.py
+++ b/tests/nnx/nn/embed_test.py
@@ -59,7 +59,7 @@ def test_nnx_linen_equivalence(
       NUM_EMBEDDINGS, IN_FEATURES, dtype=dtype, param_dtype=param_dtype
     )
     variables = model.init(key, x)
-    model_nnx.embedding.value = variables['params']['embedding']
+    model_nnx.embedding.set_value(variables['params']['embedding'])
 
     out_nnx = model_nnx(x)
     out = model.apply(variables, x)
diff --git a/tests/nnx/nn/linear_test.py b/tests/nnx/nn/linear_test.py
index a69c37765..5b7e89749 100644
--- a/tests/nnx/nn/linear_test.py
+++ b/tests/nnx/nn/linear_test.py
@@ -121,9 +121,9 @@ def test_nnx_linear_equivalence(
       dot_general=dot_general,
     )
     variables = model.init(key, x)
-    model_nnx.kernel.value = variables['params']['kernel']
+    model_nnx.kernel.set_value(variables['params']['kernel'])
     if use_bias:
-      model_nnx.bias.value = variables['params']['bias']
+      model_nnx.bias.set_value(variables['params']['bias'])
 
     out_nnx = model_nnx(x)
     out = model.apply(variables, x)
@@ -184,10 +184,10 @@ def test_nnx_einsum_equivalence(
     np.testing.assert_array_equal(out, out_nnx)
 
     variables = model.init(key, x)
-    model_nnx.kernel.value = variables['params']['kernel']
+    model_nnx.kernel.set_value(variables['params']['kernel'])
     if bias_shape is not None:
       assert model_nnx.bias is not None
-      model_nnx.bias.value = variables['params']['bias']
+      model_nnx.bias.set_value(variables['params']['bias'])
     out_nnx = model_nnx(x)
     out = model.apply(variables, x)
     assert isinstance(out, jax.Array)
diff --git a/tests/nnx/nn/normalization_test.py b/tests/nnx/nn/normalization_test.py
index 2651056c2..bd5d1dd70 100644
--- a/tests/nnx/nn/normalization_test.py
+++ b/tests/nnx/nn/normalization_test.py
@@ -241,8 +241,8 @@ def __call__(self, x, *, mask=None):
       use_fast_variance=use_fast_variance,
       rngs=rngs,
     )
-    nnx_model.linear.kernel.value = variables['params']['linear']['kernel']
-    nnx_model.linear.bias.value = variables['params']['linear']['bias']
+    nnx_model.linear.kernel.set_value(variables['params']['linear']['kernel'])
+    nnx_model.linear.bias.set_value(variables['params']['linear']['bias'])
 
     nnx_out = nnx_model(x, mask=mask)
     assert isinstance(linen_out, jax.Array)
@@ -468,20 +468,20 @@ def __call__(self, x):
     )
     # Setup the same weights and batch stats
     var_params_seq_0 = variables['params']['seq']['layers_0']
-    nnx_model.seq.layers[0].kernel.value = var_params_seq_0['kernel']
-    nnx_model.seq.layers[0].bias.value = var_params_seq_0['bias']
+    nnx_model.seq.layers[0].kernel.set_value(var_params_seq_0['kernel'])
+    nnx_model.seq.layers[0].bias.set_value(var_params_seq_0['bias'])
 
     var_params_seq_2 = variables['params']['seq']['layers_2']
-    nnx_model.seq.layers[2].scale.value = var_params_seq_2['scale']
-    nnx_model.seq.layers[2].bias.value = var_params_seq_0['bias']
+    nnx_model.seq.layers[2].scale.set_value(var_params_seq_2['scale'])
+    nnx_model.seq.layers[2].bias.set_value(var_params_seq_0['bias'])
 
     var_norm_layer = variables['batch_stats']['norm_layer']
     nnx_model.norm_layer.batch_stats[
       ('layers', 0, 'kernel', 'u')
-    ].value = var_norm_layer['seq/layers_0/kernel/u']
+    ].set_value(var_norm_layer['seq/layers_0/kernel/u'])
     nnx_model.norm_layer.batch_stats[
       ('layers', 0, 'kernel', 'sigma')
-    ].value = var_norm_layer['seq/layers_0/kernel/sigma']
+    ].set_value(var_norm_layer['seq/layers_0/kernel/sigma'])
 
     linen_out = linen_model.apply(variables, x, mutable=['batch_stats'])
     nnx_out = nnx_model(x)
diff --git a/tests/nnx/optimizer_test.py b/tests/nnx/optimizer_test.py
index c05f9dd4d..d9e3b8faa 100644
--- a/tests/nnx/optimizer_test.py
+++ b/tests/nnx/optimizer_test.py
@@ -87,7 +87,7 @@ def test_sharding_propagation(self):
 
     self.assertEqual(state['opt_state'][0]['mu']['kernel'].sharding_names, ('a', 'b'))
     self.assertEqual(
-      partition_spec['opt_state'][0]['mu']['kernel'].value,
+      partition_spec['opt_state'][0]['mu']['kernel'].get_value(),
       jax.sharding.PartitionSpec('a', 'b'),
     )
 
diff --git a/tests/nnx/spmd_test.py b/tests/nnx/spmd_test.py
index 4138a9922..646af30d3 100644
--- a/tests/nnx/spmd_test.py
+++ b/tests/nnx/spmd_test.py
@@ -175,9 +175,9 @@ def __call__(self, x: jax.Array):
     self.assertEqual(badds, [(0, 'layers'), (0, 'layers')])
     self.assertEqual(bremoves, [(0, 'layers')])
 
-  @parameterized.product(use_ref=[True, False])
-  def test_logical_rules(self, use_ref):
-    self.enter_context(nnx.use_refs(use_ref))
+  @parameterized.product(use_hijax=[True, False])
+  def test_logical_rules(self, use_hijax):
+    self.enter_context(nnx.use_hijax(use_hijax))
     class Foo(nnx.Module):
 
       def __init__(self):
diff --git a/tests/nnx/transforms_test.py b/tests/nnx/transforms_test.py
index d79aa35c8..eae485dfe 100644
--- a/tests/nnx/transforms_test.py
+++ b/tests/nnx/transforms_test.py
@@ -422,13 +422,13 @@ class TestEvalShape(absltest.TestCase):
   def test_eval_shape(self):
     abs_model = nnx.eval_shape(lambda: nnx.Linear(1, 2, rngs=nnx.Rngs(0)))
     self.assertIsInstance(abs_model, nnx.Linear)
-    self.assertIsInstance(abs_model.kernel.value, jax.ShapeDtypeStruct)
+    self.assertIsInstance(abs_model.kernel.get_value(), jax.ShapeDtypeStruct)
 
   def test_eval_shape_mutable_array(self):
-    with nnx.use_refs(True):
+    with nnx.use_hijax(True):
       abs_model = nnx.eval_shape(lambda: nnx.Linear(1, 2, rngs=nnx.Rngs(0)))
     self.assertIsInstance(abs_model, nnx.Linear)
-    self.assertIsInstance(abs_model.kernel.value, jax.ShapeDtypeStruct)
+    self.assertIsInstance(abs_model.kernel.get_value(), jax.ShapeDtypeStruct)
     self.assertEqual(abs_model.kernel.shape, (1, 2))
 
 class TestShardMap(absltest.TestCase):
@@ -782,7 +782,7 @@ def loss_fn(l1: list[nnx.Linear], l2: list[nnx.Linear]):
       loss = jnp.mean(l1[0].kernel * l2[0].kernel) + jnp.mean(
         l1[0].bias * l2[0].bias
       )
-      l1[0].kernel.value = jnp.array(-1.0)
+      l1[0].kernel.set_value(jnp.array(-1.0))
       m3 = nnx.Linear(2, 3, rngs=nnx.Rngs(2))
       return loss, m3
 
diff --git a/tests/nnx/variable_test.py b/tests/nnx/variable_test.py
index 362519f81..ec2922343 100644
--- a/tests/nnx/variable_test.py
+++ b/tests/nnx/variable_test.py
@@ -25,12 +25,12 @@
 class TestVariable(absltest.TestCase):
   def test_pytree(self):
     r1 = nnx.Param(1)
-    self.assertEqual(r1.value, 1)
+    self.assertEqual(r1.get_value(), 1)
 
     r2 = jax.tree.map(lambda x: x + 1, r1)
 
-    self.assertEqual(r1.value, 1)
-    self.assertEqual(r2.value, 2)
+    self.assertEqual(r1.get_value(), 1)
+    self.assertEqual(r2.get_value(), 2)
     self.assertIsNot(r1, r2)
 
   def test_overloads_module(self):
@@ -94,38 +94,41 @@ def test_binary_ops(self):
     self.assertEqual(v1[...], 5)
 
   def test_mutable_array_context(self):
-    with nnx.use_refs(False):
+    with nnx.use_hijax(False):
       v = nnx.Variable(jnp.array(1.0))
-      self.assertFalse(nnx.using_refs())
-      self.assertNotIsInstance(v.raw_value, jax.Ref)
+      self.assertFalse(nnx.using_hijax())
+      self.assertNotIsInstance(v[...], jax.Ref)
 
-      with nnx.use_refs(True):
+      with nnx.use_hijax(True):
         v = nnx.Variable(jnp.array(1.0))
-        self.assertTrue(nnx.using_refs())
-        self.assertIsInstance(v.raw_value, jax.Ref)
+        self.assertTrue(nnx.using_hijax())
+        self.assertIsInstance(v[...], jax.Array)
 
       v = nnx.Variable(jnp.array(2.0))
-      self.assertNotIsInstance(v.raw_value, jax.Ref)
-      self.assertFalse(nnx.using_refs())
+      self.assertIsInstance(v[...], jax.Array)
+      self.assertFalse(nnx.using_hijax())
 
-      nnx.use_refs(True)
+      nnx.use_hijax(True)
 
       v = nnx.Variable(jnp.array(0.0))
-      self.assertTrue(nnx.using_refs())
-      self.assertIsInstance(v.raw_value, jax.Ref)
+      self.assertTrue(nnx.using_hijax())
+      self.assertIsInstance(v[...], jax.Array)
 
     v = nnx.Variable(jnp.array(1.0))
-    self.assertFalse(nnx.using_refs())
-    self.assertNotIsInstance(v.raw_value, jax.Ref)
+    self.assertFalse(nnx.using_hijax())
+    self.assertIsInstance(v[...], jax.Array)
 
   def test_get_set_metadata(self):
     v = nnx.Variable(jnp.array(1.0))
-    self.assertEqual(v.get_metadata(), {})
+    self.assertEqual(v.get_metadata(), {'is_hijax': False})
     v.set_metadata(a=1, b=2)
     self.assertEqual(v.get_metadata('a'), 1)
     self.assertEqual(v.get_metadata('b'), 2)
-    v.set_metadata({'b': 3, 'c': 4})
-    self.assertEqual(v.get_metadata(), {'b': 3, 'c': 4})
+    v.set_metadata({'b': 3, 'c': 4, 'is_hijax': False})
+    self.assertEqual(
+      v.get_metadata(),
+      {'b': 3, 'c': 4, 'is_hijax': False},
+    )
     self.assertEqual(v.get_metadata('b'), 3)
     self.assertEqual(v.get_metadata('c'), 4)
     c = v.get_metadata('c')