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<div class="section" id="torch-fx-experimental">
<h1>torch.fx.experimental<a class="headerlink" href="#torch-fx-experimental" title="Permalink to this heading">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>These APIs are experimental and subject to change without notice.</p>
</div>
<div class="section" id="module-torch.fx.experimental.symbolic_shapes">
<span id="torch-fx-experimental-symbolic-shapes"></span><h2>torch.fx.experimental.symbolic_shapes<a class="headerlink" href="#module-torch.fx.experimental.symbolic_shapes" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.ShapeEnv"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html#torch.fx.experimental.symbolic_shapes.ShapeEnv" title="torch.fx.experimental.symbolic_shapes.ShapeEnv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ShapeEnv</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.DimDynamic"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html#torch.fx.experimental.symbolic_shapes.DimDynamic" title="torch.fx.experimental.symbolic_shapes.DimDynamic"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DimDynamic</span></code></a></p></td>
<td><p>Controls how to perform symbol allocation for a dimension.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint" title="torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StrictMinMaxConstraint</span></code></a></p></td>
<td><p>For clients: the size at this dimension must be within 'vr' (which specifies a lower and upper bound, inclusive-inclusive) AND it must be non-negative and should not be 0 or 1 (but see NB below).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html#torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint" title="torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RelaxedUnspecConstraint</span></code></a></p></td>
<td><p>For clients: no explicit constraint; constraint is whatever is implicitly inferred by guards from tracing.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.EqualityConstraint"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html#torch.fx.experimental.symbolic_shapes.EqualityConstraint" title="torch.fx.experimental.symbolic_shapes.EqualityConstraint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EqualityConstraint</span></code></a></p></td>
<td><p>Represent and decide various kinds of equality constraints between input sources.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.SymbolicContext"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html#torch.fx.experimental.symbolic_shapes.SymbolicContext" title="torch.fx.experimental.symbolic_shapes.SymbolicContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SymbolicContext</span></code></a></p></td>
<td><p>Data structure specifying how we should create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code>; e.g., should they be static or dynamic.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html#torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext" title="torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StatelessSymbolicContext</span></code></a></p></td>
<td><p>Create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code> via a symbolic_context determination as given by <code class="docutils literal notranslate"><span class="pre">DimDynamic</span></code> and <code class="docutils literal notranslate"><span class="pre">DimConstraint</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html#torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext" title="torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StatefulSymbolicContext</span></code></a></p></td>
<td><p>Create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code> via a symbolic_context determination as given by a cache of Source:Symbol.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html#torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext" title="torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SubclassSymbolicContext</span></code></a></p></td>
<td><p>The correct symbolic context for a given inner tensor of a traceable tensor subclass may differ from that of the outer symbolic context.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.DimConstraints"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html#torch.fx.experimental.symbolic_shapes.DimConstraints" title="torch.fx.experimental.symbolic_shapes.DimConstraints"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DimConstraints</span></code></a></p></td>
<td><p>Custom solver for a system of constraints on symbolic dimensions.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.ShapeEnvSettings"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html#torch.fx.experimental.symbolic_shapes.ShapeEnvSettings" title="torch.fx.experimental.symbolic_shapes.ShapeEnvSettings"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ShapeEnvSettings</span></code></a></p></td>
<td><p>Encapsulates all shape env settings that could potentially affect FakeTensor dispatch.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.ConvertIntKey"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html#torch.fx.experimental.symbolic_shapes.ConvertIntKey" title="torch.fx.experimental.symbolic_shapes.ConvertIntKey"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvertIntKey</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.CallMethodKey"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html#torch.fx.experimental.symbolic_shapes.CallMethodKey" title="torch.fx.experimental.symbolic_shapes.CallMethodKey"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CallMethodKey</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts" title="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PropagateUnbackedSymInts</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.DivideByKey"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html#torch.fx.experimental.symbolic_shapes.DivideByKey" title="torch.fx.experimental.symbolic_shapes.DivideByKey"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DivideByKey</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.InnerTensorKey"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html#torch.fx.experimental.symbolic_shapes.InnerTensorKey" title="torch.fx.experimental.symbolic_shapes.InnerTensorKey"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InnerTensorKey</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.hint_int"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.hint_int.html#torch.fx.experimental.symbolic_shapes.hint_int" title="torch.fx.experimental.symbolic_shapes.hint_int"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hint_int</span></code></a></p></td>
<td><p>Retrieve the hint for an int (based on the underlying real values as observed at runtime).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.is_concrete_int"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html#torch.fx.experimental.symbolic_shapes.is_concrete_int" title="torch.fx.experimental.symbolic_shapes.is_concrete_int"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_concrete_int</span></code></a></p></td>
<td><p>Utility to check if underlying object in SymInt is concrete value.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.is_concrete_bool"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html#torch.fx.experimental.symbolic_shapes.is_concrete_bool" title="torch.fx.experimental.symbolic_shapes.is_concrete_bool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_concrete_bool</span></code></a></p></td>
<td><p>Utility to check if underlying object in SymBool is concrete value.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.is_concrete_float"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html#torch.fx.experimental.symbolic_shapes.is_concrete_float" title="torch.fx.experimental.symbolic_shapes.is_concrete_float"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_concrete_float</span></code></a></p></td>
<td><p>Utility to check if underlying object in SymInt is concrete value.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.has_free_symbols"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html#torch.fx.experimental.symbolic_shapes.has_free_symbols" title="torch.fx.experimental.symbolic_shapes.has_free_symbols"><code class="xref py py-obj docutils literal notranslate"><span class="pre">has_free_symbols</span></code></a></p></td>
<td><p>Faster version of bool(free_symbols(val))</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html#torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols" title="torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols"><code class="xref py py-obj docutils literal notranslate"><span class="pre">has_free_unbacked_symbols</span></code></a></p></td>
<td><p>Faster version of bool(free_unbacked_symbols(val))</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.definitely_true"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.definitely_true.html#torch.fx.experimental.symbolic_shapes.definitely_true" title="torch.fx.experimental.symbolic_shapes.definitely_true"><code class="xref py py-obj docutils literal notranslate"><span class="pre">definitely_true</span></code></a></p></td>
<td><p>Returns True only if we can tell that a is True, possibly introducing a guard in the process.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.definitely_false"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.definitely_false.html#torch.fx.experimental.symbolic_shapes.definitely_false" title="torch.fx.experimental.symbolic_shapes.definitely_false"><code class="xref py py-obj docutils literal notranslate"><span class="pre">definitely_false</span></code></a></p></td>
<td><p>Returns True only if we can tell that a is False, possibly introducing a guard in the process.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.guard_size_oblivious"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html#torch.fx.experimental.symbolic_shapes.guard_size_oblivious" title="torch.fx.experimental.symbolic_shapes.guard_size_oblivious"><code class="xref py py-obj docutils literal notranslate"><span class="pre">guard_size_oblivious</span></code></a></p></td>
<td><p>Perform a guard on a symbolic boolean expression in a size oblivious way.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.sym_eq"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.sym_eq.html#torch.fx.experimental.symbolic_shapes.sym_eq" title="torch.fx.experimental.symbolic_shapes.sym_eq"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sym_eq</span></code></a></p></td>
<td><p>Like ==, but when run on list/tuple, it will recursively test equality and use sym_and to join the results together, without guarding.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.constrain_range"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.constrain_range.html#torch.fx.experimental.symbolic_shapes.constrain_range" title="torch.fx.experimental.symbolic_shapes.constrain_range"><code class="xref py py-obj docutils literal notranslate"><span class="pre">constrain_range</span></code></a></p></td>
<td><p>Applies a constraint that the passed in SymInt must lie between min-max inclusive-inclusive, WITHOUT introducing a guard on the SymInt (meaning that it can be used on unbacked SymInts).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.constrain_unify"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html#torch.fx.experimental.symbolic_shapes.constrain_unify" title="torch.fx.experimental.symbolic_shapes.constrain_unify"><code class="xref py py-obj docutils literal notranslate"><span class="pre">constrain_unify</span></code></a></p></td>
<td><p>Given two SymInts, constrain them so that they must be equal.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html#torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr" title="torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">canonicalize_bool_expr</span></code></a></p></td>
<td><p>Canonicalize a boolean expression by transforming it into a lt / le inequality and moving all the non-constant terms to the rhs.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.statically_known_true"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.statically_known_true.html#torch.fx.experimental.symbolic_shapes.statically_known_true" title="torch.fx.experimental.symbolic_shapes.statically_known_true"><code class="xref py py-obj docutils literal notranslate"><span class="pre">statically_known_true</span></code></a></p></td>
<td><p>Returns True if x can be simplified to a constant and is true.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.lru_cache"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.lru_cache.html#torch.fx.experimental.symbolic_shapes.lru_cache" title="torch.fx.experimental.symbolic_shapes.lru_cache"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lru_cache</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.check_consistent"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.check_consistent.html#torch.fx.experimental.symbolic_shapes.check_consistent" title="torch.fx.experimental.symbolic_shapes.check_consistent"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_consistent</span></code></a></p></td>
<td><p>Test that two "meta" values (typically either Tensor or SymInt) have the same values, e.g., after retracing.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html#torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings" title="torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_unbacked_bindings</span></code></a></p></td>
<td><p>After having run fake tensor propagation and producing example_value result, traverse example_value looking for freshly bound unbacked symbols and record their paths for later.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.rebind_unbacked"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html#torch.fx.experimental.symbolic_shapes.rebind_unbacked" title="torch.fx.experimental.symbolic_shapes.rebind_unbacked"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rebind_unbacked</span></code></a></p></td>
<td><p>Suppose we are retracing a pre-existing FX graph that previously had fake tensor propagation (and therefore unbacked SymInts).</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html#torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings" title="torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resolve_unbacked_bindings</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.is_accessor_node"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html#torch.fx.experimental.symbolic_shapes.is_accessor_node" title="torch.fx.experimental.symbolic_shapes.is_accessor_node"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_accessor_node</span></code></a></p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-torch.fx.experimental.proxy_tensor">
<span id="torch-fx-experimental-proxy-tensor"></span><h2>torch.fx.experimental.proxy_tensor<a class="headerlink" href="#module-torch.fx.experimental.proxy_tensor" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.proxy_tensor.make_fx"/><a class="reference internal" href="generated/torch.fx.experimental.proxy_tensor.make_fx.html#torch.fx.experimental.proxy_tensor.make_fx" title="torch.fx.experimental.proxy_tensor.make_fx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_fx</span></code></a></p></td>
<td><p>Given a function f, return a new function which when executed with valid arguments to f, returns an FX GraphModule representing the set of operations that were executed during the course of execution.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.proxy_tensor.handle_sym_dispatch"/><a class="reference internal" href="generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html#torch.fx.experimental.proxy_tensor.handle_sym_dispatch" title="torch.fx.experimental.proxy_tensor.handle_sym_dispatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">handle_sym_dispatch</span></code></a></p></td>
<td><p>Call into the currently active proxy tracing mode to do a SymInt/SymFloat/SymBool dispatch trace on a function that operates on these arguments.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.proxy_tensor.get_proxy_mode"/><a class="reference internal" href="generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html#torch.fx.experimental.proxy_tensor.get_proxy_mode" title="torch.fx.experimental.proxy_tensor.get_proxy_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_proxy_mode</span></code></a></p></td>
<td><p>Current the currently active proxy tracing mode, or None if we are not currently tracing.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.proxy_tensor.maybe_enable_thunkify"/><a class="reference internal" href="generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html#torch.fx.experimental.proxy_tensor.maybe_enable_thunkify" title="torch.fx.experimental.proxy_tensor.maybe_enable_thunkify"><code class="xref py py-obj docutils literal notranslate"><span class="pre">maybe_enable_thunkify</span></code></a></p></td>
<td><p>Within this context manager, if you are doing make_fx tracing, we will thunkify all SymNode compute and avoid tracing it into the graph unless it is actually needed.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.proxy_tensor.maybe_disable_thunkify"/><a class="reference internal" href="generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html#torch.fx.experimental.proxy_tensor.maybe_disable_thunkify" title="torch.fx.experimental.proxy_tensor.maybe_disable_thunkify"><code class="xref py py-obj docutils literal notranslate"><span class="pre">maybe_disable_thunkify</span></code></a></p></td>
<td><p>Within a context, disable thunkification.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
</article>
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