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fastai_typing.html
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---
title: fastai_typing
keywords: fastai
sidebar: home_sidebar
summary: "Type annotations names"
---
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<h2 id="Type-abbreviations">Type abbreviations<a class="anchor-link" href="#Type-abbreviations">¶</a></h2>
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<p>The code and docs sometimes use <em>type abbreviations</em> to avoid type signatures getting unwieldy. Here's a list of all abbreviations for composite types for convenient access.</p>
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<h2 id="From-core">From <a href="/core.html#core"><code>core</code></a><a class="anchor-link" href="#From-core">¶</a></h2>
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<ul>
<li><code>AnnealFunc</code> = <code>Callable</code>[[<code>Number</code>,<code>Number</code>,<code>float</code>], <code>Number</code>]</li>
<li><code>ArgStar</code> = <code>Collection</code>[<code>Any</code>]</li>
<li><code>BatchSamples</code> = <code>Collection</code>[<code>Tuple</code>[<code>Collection</code>[<code>int</code>], <code>int</code>]]</li>
<li><code>Classes</code> = <code>Collection</code>[<code>Any</code>]</li>
<li><code>DataFrameOrChunks</code> = <code>Union[DataFrame, pd.io.parsers.TextFileReader]</code></li>
<li><code>FilePathList</code> = <code>Collection</code>[<code>Path</code>]</li>
<li><code>Floats</code> = <code>Union</code>[<code>float</code>, <code>Collection</code>[<code>float</code>]]</li>
<li><code>ImgLabels</code> = <code>Collection</code>[<code>ImgLabel</code>]</li>
<li><code>KeyFunc</code> = <code>Callable</code>[[<code>int</code>], <code>int</code>]</li>
<li><code>KWArgs</code> = <code>Dict</code>[<code>str</code>,<code>Any</code>]</li>
<li><code>ListOrItem</code> = <code>Union</code>[<code>Collection</code>[<code>Any</code>],<code>int</code>,<code>float</code>,<code>str</code>]</li>
<li><code>ListRules</code> = <code>Collection</code>[<code>Callable</code>[[<code>str</code>],<code>str</code>]]</li>
<li><code>ListSizes</code> = <code>Collection</code>[<code>Tuple</code>[<code>int</code>,<code>int</code>]]</li>
<li><code>NPArrayableList</code> = <code>Collection</code>[<code>Union</code>[<code>np</code>.<code>ndarray</code>, <code>list</code>]]</li>
<li><code>NPArrayList</code> = <code>Collection</code>[<code>np</code>.<code>ndarray</code>]</li>
<li><code>OptDataFrame</code> = <code>Optional</code>[<code>DataFrame</code>]</li>
<li><code>OptListOrItem</code> = <code>Optional</code>[<code>ListOrItem</code>]</li>
<li><code>OptRange</code> = <code>Optional</code>[<code>Tuple</code>[<code>float</code>,<code>float</code>]]</li>
<li><code>OptStrTuple</code> = <code>Optional</code>[<code>Tuple</code>[<code>str</code>,<code>str</code>]]</li>
<li><code>OptStats</code> = <code>Optional</code>[<code>Tuple</code>[<code>np</code>.<code>ndarray</code>, <code>np</code>.<code>ndarray</code>]]</li>
<li><code>PathOrStr</code> = <code>Union</code>[<code>Path</code>,<code>str</code>]</li>
<li><code>PBar</code> = <code>Union</code>[<code>MasterBar</code>, <code>ProgressBar</code>]</li>
<li><code>Point</code>=<code>Tuple</code>[<code>float</code>,<code>float</code>]</li>
<li><code>Points</code>=<code>Collection</code>[<code>Point</code>]</li>
<li><code>Sizes</code> = <code>List</code>[<code>List</code>[<code>int</code>]]</li>
<li><code>SplitArrayList</code> = <code>List</code>[<code>Tuple</code>[<code>np</code>.<code>ndarray</code>,<code>np</code>.<code>ndarray</code>]]</li>
<li><code>StartOptEnd</code>=<code>Union</code>[<code>float</code>,<code>Tuple</code>[<code>float</code>,<code>float</code>]]</li>
<li><code>StrList</code> = <code>Collection</code>[<code>str</code>]</li>
<li><code>Tokens</code> = <code>Collection</code>[<code>Collection</code>[<code>str</code>]]</li>
<li><code>OptStrList</code> = <code>Optional</code>[<code>StrList</code>]</li>
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<h2 id="From-torch_core">From <a href="/torch_core.html#torch_core"><code>torch_core</code></a><a class="anchor-link" href="#From-torch_core">¶</a></h2>
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<ul>
<li><code>BoolOrTensor</code> = <code>Union</code>[<code>bool</code>,<code>Tensor</code>]</li>
<li><code>FloatOrTensor</code> = <code>Union</code>[<code>float</code>,<code>Tensor</code>]</li>
<li><code>IntOrTensor</code> = <code>Union</code>[<code>int</code>,<code>Tensor</code>]</li>
<li><code>ItemsList</code> = <code>Collection</code>[<code>Union</code>[<code>Tensor</code>,<a href="/core.html#ItemBase"><code>ItemBase</code></a>,'<code>ItemsList</code>',<code>float</code>,<code>int</code>]]</li>
<li><code>LambdaFunc</code> = <code>Callable</code>[[<code>Tensor</code>],<code>Tensor</code>]</li>
<li><code>LayerFunc</code> = <code>Callable</code>[ [<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>nn.Module</code></a>],<code>None</code>]</li>
<li><a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Model</code></a> = <a href="https://pytorch.org/docs/stable/nn.html#torch-nn"><code>nn</code></a>.<code>Module</code></li>
<li><code>ModuleList</code> = <code>Collection</code>[<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>nn.Module</code></a>]</li>
<li><code>OptOptimizer</code> = <code>Optional</code>[<a href="https://pytorch.org/docs/stable/optim.html#torch.optim.Optimizer"><code>optim.Optimizer</code></a>]</li>
<li><code>ParamList</code> = <code>Collection</code>[<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Parameter"><code>nn.Parameter</code></a>]</li>
<li><code>Rank0Tensor</code> = <code>NewType</code>('<code>OneEltTensor</code>', <code>Tensor</code>)</li>
<li><code>SplitFunc</code> = <code>Callable</code>[<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Model</code></a>], <code>List</code><a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Model</code></a>]]</li>
<li><code>SplitFuncOrIdxList</code> = <code>Union</code>[<code>Callable</code>, <code>Collection</code>[<code>ModuleList</code>]]</li>
<li><code>TensorOrNumber</code> = <code>Union</code>[<code>Tensor</code>,<code>Number</code>]</li>
<li><code>TensorOrNumList</code> = <code>Collection</code>[<code>TensorOrNumber</code>]</li>
<li><code>TensorImageSize</code> = <code>Tuple</code>[<code>int</code>,<code>int</code>,<code>int</code>]</li>
<li><code>Tensors</code> = <code>Union</code>[<code>Tensor</code>, <code>Collection</code>['<code>Tensors</code>']]</li>
<li><code>Weights</code> = <code>Dict</code>[<code>str</code>,<code>Tensor</code>]</li>
<li><code>AffineFunc</code> = <code>Callable</code>[[<code>KWArgs</code>], <a href="https://pytorch.org/docs/stable/tensors.html#torch-tensor"><code>AffineMatrix</code></a>]</li>
<li><code>HookFunc</code> = <code>Callable</code>[<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Model</code></a>, <code>Tensors</code>, <code>Tensors</code>], <code>Any</code>]</li>
<li><code>LogitTensorImage</code> = <code>TensorImage</code></li>
<li><code>LossFunction</code> = <code>Callable</code>[[<code>Tensor</code>, <code>Tensor</code>], <code>Rank0Tensor</code>]</li>
<li><code>MetricFunc</code> = <code>Callable</code>[[<code>Tensor</code>,<code>Tensor</code>],<code>TensorOrNumber</code>]</li>
<li><code>MetricFuncList</code> = <code>Collection</code>[<code>MetricFunc</code>]</li>
<li><code>MetricsList</code> = <code>Collection</code>[<code>TensorOrNumber</code>]</li>
<li><code>OptLossFunc</code> = <code>Optional</code>[<code>LossFunction</code>]</li>
<li><code>OptMetrics</code> = <code>Optional</code>[<code>MetricsList</code>]</li>
<li><code>OptSplitFunc</code> = <code>Optional</code>[<code>SplitFunc</code>]</li>
<li><code>PixelFunc</code> = <code>Callable</code>[[<code>TensorImage</code>, <code>ArgStar</code>, <code>KWArgs</code>], <code>TensorImage</code>]</li>
<li><code>CoordFunc</code> = <code>Callable</code>[<a href="/vision.image.html#FlowField"><code>FlowField</code></a>, <code>TensorImageSize</code>, <code>ArgStar</code>, <code>KWArgs</code>], <code>LogitTensorImage</code>]</li>
<li><code>LightingFunc</code> = <code>Callable</code>[[<code>LogitTensorImage</code>, <code>ArgStar</code>, <code>KWArgs</code>], <code>LogitTensorImage</code>]</li>
</ul>
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