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Merge pull request #469 from Seasawher/auto-update-branch
Lean/Mathlib update
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docs/attributes.md

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# Attributes
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Mathlib version: `3b5ca69666326212946f476ff8fd2cbc4796fb91`
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Mathlib version: `10061bf49d6d842b2099878901410ef3b6a393c2`
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## Std.Internal.tree_tac
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simp theorems used by internal DTreeMap lemmas
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```lean
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-MonoidalCategory.whiskerLeft_id, -MonoidalCategory.id_whiskerRight,
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-MonoidalCategory.tensor_comp, -MonoidalCategory.tensor_comp_assoc,
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-Mon_Class.mul_assoc, -Mon_Class.mul_assoc_assoc
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-MonObj.mul_assoc, -MonObj.mul_assoc_assoc
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```
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The general algorithm it follows is to push the associators `α_` and commutators `β_` inwards until

docs/commands.md

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# Commands
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Mathlib version: `3b5ca69666326212946f476ff8fd2cbc4796fb91`
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Mathlib version: `10061bf49d6d842b2099878901410ef3b6a393c2`
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## \#adaptation_note
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Defined in: `adaptationNoteCmd`

docs/options.md

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# Options
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Mathlib version: `3b5ca69666326212946f476ff8fd2cbc4796fb91`
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Mathlib version: `10061bf49d6d842b2099878901410ef3b6a393c2`
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## Elab.async
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type: `Bool`
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enable the tactic analysis framework
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## linter.tacticAnalysis.introMerge
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type: `Bool`
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default: `true`
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## linter.tacticAnalysis.linarithToGrind
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type: `Bool`
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docs/tactics.md

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# Tactics
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Mathlib version: `3b5ca69666326212946f476ff8fd2cbc4796fb91`
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Mathlib version: `10061bf49d6d842b2099878901410ef3b6a393c2`
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## \#adaptation_note
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Defined in: `«tactic#adaptation_note_»`
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(`true` by default.)
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* `restrict_type` (not yet implemented in mathlib4)
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will only use hypotheses that are inequalities over `tp`. This is useful
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if you have e.g. both integer and rational valued inequalities in the local context, which can
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if you have e.g. both integer- and rational-valued inequalities in the local context, which can
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sometimes confuse the tactic.
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A variant, `nlinarith`, does some basic preprocessing to handle some nonlinear goals.
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(`true` by default.)
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* `restrict_type` (not yet implemented in mathlib4)
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will only use hypotheses that are inequalities over `tp`. This is useful
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if you have e.g. both integer and rational valued inequalities in the local context, which can
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if you have e.g. both integer- and rational-valued inequalities in the local context, which can
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sometimes confuse the tactic.
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A variant, `nlinarith`, does some basic preprocessing to handle some nonlinear goals.

lake-manifest.json

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"type": "git",
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"subDir": null,
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"scope": "",
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"rev": "3b5ca69666326212946f476ff8fd2cbc4796fb91",
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"rev": "10061bf49d6d842b2099878901410ef3b6a393c2",
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"name": "mathlib",
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"manifestFile": "lake-manifest.json",
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"inputRev": "master",

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