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diff --git a/.doctrees/overview/related_work/ivy_vs_onnx.doctree b/.doctrees/overview/related_work/ivy_vs_onnx.doctree
index 2564294a1..b2294799e 100644
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diff --git a/_sources/overview/related_work/ivy_vs_onnx.rst.txt b/_sources/overview/related_work/ivy_vs_onnx.rst.txt
index 639510fbe..ba27a9c09 100644
--- a/_sources/overview/related_work/ivy_vs_onnx.rst.txt
+++ b/_sources/overview/related_work/ivy_vs_onnx.rst.txt
@@ -9,8 +9,8 @@ Comparing Ivy with ONNX
 .. |onnx| image:: https://raw.githubusercontent.com/unifyai/unifyai.github.io/main/img/externally_linked/related_work/exchange_formats/onnx.png
     :height: 20pt
     :class: dark-light
-.. |ivy| image:: https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.svg
-    :height: 26pt
+.. |ivy| image:: https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.png
+    :height: 40pt
     :class: dark-light
 
 ONNX |onnx|
diff --git a/docs/functional/ivy/ivy.functional.ivy.meta.html b/docs/functional/ivy/ivy.functional.ivy.meta.html
index 1e6018782..b1f736459 100644
--- a/docs/functional/ivy/ivy.functional.ivy.meta.html
+++ b/docs/functional/ivy/ivy.functional.ivy.meta.html
@@ -1334,7 +1334,7 @@ <h1>Meta<a class="headerlink" href="#meta" title="Link to this heading">#</a></h
 <li><p><strong>variables</strong> (<a class="reference internal" href="../../data_classes/data_classes/ivy.data_classes.container.html#ivy.data_classes.container.container.Container" title="ivy.data_classes.container.container.Container"><code class="xref py py-class docutils literal notranslate"><span class="pre">Container</span></code></a>) – Variables to be optimized during the meta step</p></li>
 <li><p><strong>inner_grad_steps</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of gradient steps to perform during the inner loop.</p></li>
 <li><p><strong>inner_learning_rate</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – The learning rate of the inner loop.</p></li>
-<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7fd9547708b0&gt;</span></code>) – The function used for the inner loop optimization.
+<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7f95b8ebc8b0&gt;</span></code>) – The function used for the inner loop optimization.
 Default is ivy.gradient_descent_update.</p></li>
 <li><p><strong>inner_batch_fn</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – Function to apply to the task sub-batch, before passing to the inner_cost_fn.
 Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
@@ -1388,7 +1388,7 @@ <h1>Meta<a class="headerlink" href="#meta" title="Link to this heading">#</a></h
 <li><p><strong>variables</strong> (<a class="reference internal" href="../../data_classes/data_classes/ivy.data_classes.container.html#ivy.data_classes.container.container.Container" title="ivy.data_classes.container.container.Container"><code class="xref py py-class docutils literal notranslate"><span class="pre">Container</span></code></a>) – Variables to be optimized during the meta step</p></li>
 <li><p><strong>inner_grad_steps</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of gradient steps to perform during the inner loop.</p></li>
 <li><p><strong>inner_learning_rate</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – The learning rate of the inner loop.</p></li>
-<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7fd9547708b0&gt;</span></code>) – The function used for the inner loop optimization.
+<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7f95b8ebc8b0&gt;</span></code>) – The function used for the inner loop optimization.
 Default is ivy.gradient_descent_update.</p></li>
 <li><p><strong>inner_batch_fn</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – Function to apply to the task sub-batch, before passing to the inner_cost_fn.
 Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
@@ -1465,7 +1465,7 @@ <h1>Meta<a class="headerlink" href="#meta" title="Link to this heading">#</a></h
 <li><p><strong>variables</strong> (<a class="reference internal" href="../../data_classes/data_classes/ivy.data_classes.container.html#ivy.data_classes.container.container.Container" title="ivy.data_classes.container.container.Container"><code class="xref py py-class docutils literal notranslate"><span class="pre">Container</span></code></a>) – Variables to be optimized.</p></li>
 <li><p><strong>inner_grad_steps</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of gradient steps to perform during the inner loop.</p></li>
 <li><p><strong>inner_learning_rate</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – The learning rate of the inner loop.</p></li>
-<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7fd9547708b0&gt;</span></code>) – The function used for the inner loop optimization. It takes the learnable
+<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7f95b8ebc8b0&gt;</span></code>) – The function used for the inner loop optimization. It takes the learnable
 weights,the derivative of the cost with respect to the weights, and the learning
 rate as arguments, and returns the updated variables.
 Default is <cite>gradient_descent_update</cite>.</p></li>
diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html
index b94586858..0dd43bcb3 100644
--- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html
+++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html
@@ -1337,7 +1337,7 @@ <h1>fomaml_step<a class="headerlink" href="#fomaml-step" title="Link to this hea
 <li><p><strong>variables</strong> (<a class="reference internal" href="../../../data_classes/data_classes/ivy.data_classes.container.html#ivy.data_classes.container.container.Container" title="ivy.data_classes.container.container.Container"><code class="xref py py-class docutils literal notranslate"><span class="pre">Container</span></code></a>) – Variables to be optimized during the meta step</p></li>
 <li><p><strong>inner_grad_steps</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of gradient steps to perform during the inner loop.</p></li>
 <li><p><strong>inner_learning_rate</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – The learning rate of the inner loop.</p></li>
-<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7fd9547708b0&gt;</span></code>) – The function used for the inner loop optimization.
+<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7f95b8ebc8b0&gt;</span></code>) – The function used for the inner loop optimization.
 Default is ivy.gradient_descent_update.</p></li>
 <li><p><strong>inner_batch_fn</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – Function to apply to the task sub-batch, before passing to the inner_cost_fn.
 Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html
index f37cb04a4..8e5b7d913 100644
--- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html
+++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html
@@ -1337,7 +1337,7 @@ <h1>maml_step<a class="headerlink" href="#maml-step" title="Link to this heading
 <li><p><strong>variables</strong> (<a class="reference internal" href="../../../data_classes/data_classes/ivy.data_classes.container.html#ivy.data_classes.container.container.Container" title="ivy.data_classes.container.container.Container"><code class="xref py py-class docutils literal notranslate"><span class="pre">Container</span></code></a>) – Variables to be optimized during the meta step</p></li>
 <li><p><strong>inner_grad_steps</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of gradient steps to perform during the inner loop.</p></li>
 <li><p><strong>inner_learning_rate</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – The learning rate of the inner loop.</p></li>
-<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7fd9547708b0&gt;</span></code>) – The function used for the inner loop optimization.
+<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7f95b8ebc8b0&gt;</span></code>) – The function used for the inner loop optimization.
 Default is ivy.gradient_descent_update.</p></li>
 <li><p><strong>inner_batch_fn</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – Function to apply to the task sub-batch, before passing to the inner_cost_fn.
 Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html
index 49f65362f..35fe806a0 100644
--- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html
+++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html
@@ -1334,7 +1334,7 @@ <h1>reptile_step<a class="headerlink" href="#reptile-step" title="Link to this h
 <li><p><strong>variables</strong> (<a class="reference internal" href="../../../data_classes/data_classes/ivy.data_classes.container.html#ivy.data_classes.container.container.Container" title="ivy.data_classes.container.container.Container"><code class="xref py py-class docutils literal notranslate"><span class="pre">Container</span></code></a>) – Variables to be optimized.</p></li>
 <li><p><strong>inner_grad_steps</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of gradient steps to perform during the inner loop.</p></li>
 <li><p><strong>inner_learning_rate</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – The learning rate of the inner loop.</p></li>
-<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7fd9547708b0&gt;</span></code>) – The function used for the inner loop optimization. It takes the learnable
+<li><p><strong>inner_optimization_step</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code>, default: <code class="docutils literal notranslate"><span class="pre">&lt;function</span> <span class="pre">gradient_descent_update</span> <span class="pre">at</span> <span class="pre">0x7f95b8ebc8b0&gt;</span></code>) – The function used for the inner loop optimization. It takes the learnable
 weights,the derivative of the cost with respect to the weights, and the learning
 rate as arguments, and returns the updated variables.
 Default is <cite>gradient_descent_update</cite>.</p></li>
diff --git a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html
index fe8bae0f2..5a2c5895e 100644
--- a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html
+++ b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html
@@ -1323,7 +1323,7 @@
 <p>Should not be used inside any of the test functions.</p>
 <dl class="py data">
 <dt class="sig sig-object py" id="ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG">
-<span class="sig-prename descclassname"><span class="pre">ivy_tests.test_ivy.helpers.globals.</span></span><span class="sig-name descname"><span class="pre">CURRENT_FRONTEND_CONFIG</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">&lt;object</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd947701dc0&gt;</span></em><a class="headerlink" href="#ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ivy_tests.test_ivy.helpers.globals.</span></span><span class="sig-name descname"><span class="pre">CURRENT_FRONTEND_CONFIG</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">&lt;object</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95abdfdde0&gt;</span></em><a class="headerlink" href="#ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG" title="Link to this definition">#</a></dt>
 <dd></dd></dl>
 
 <dl class="py exception">
diff --git a/docs/stateful/ivy.stateful.layers.html b/docs/stateful/ivy.stateful.layers.html
index 7d76b3da9..762f7f736 100644
--- a/docs/stateful/ivy.stateful.layers.html
+++ b/docs/stateful/ivy.stateful.layers.html
@@ -1450,8 +1450,8 @@
 <li><p><strong>strides</strong> – The stride of the sliding window for each dimension of input.</p></li>
 <li><p><strong>padding</strong> – SAME” or “VALID” indicating the algorithm, or
 list indicating the per-dimension paddings.</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257490&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
-<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257430&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89ab160&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89ab100&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
 <li><p><strong>with_bias</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to include a bias term, default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>data_format</strong> (default: <code class="docutils literal notranslate"><span class="pre">'NWC'</span></code>) – NWC” or “NCW”. Defaults to “NWC”.</p></li>
 <li><p><strong>dilations</strong> (default: <code class="docutils literal notranslate"><span class="pre">1</span></code>) – The dilation factor for each dimension of input. (Default value = 1)</p></li>
@@ -1488,8 +1488,8 @@
 <li><p><strong>strides</strong> – The stride of the sliding window for each dimension of input.</p></li>
 <li><p><strong>padding</strong> – SAME” or “VALID” indicating the algorithm, or
 list indicating the per-dimension paddings.</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd9542573d0&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
-<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257370&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89ab0a0&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89ab040&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
 <li><p><strong>with_bias</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to include a bias term, default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>output_shape</strong> (default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – Shape of the output (Default value = None)</p></li>
 <li><p><strong>data_format</strong> (default: <code class="docutils literal notranslate"><span class="pre">'NWC'</span></code>) – NWC” or “NCW”. Defaults to “NWC”.</p></li>
@@ -1527,8 +1527,8 @@
 <li><p><strong>strides</strong> – The stride of the sliding window for each dimension of input.</p></li>
 <li><p><strong>padding</strong> – SAME” or “VALID” indicating the algorithm, or
 list indicating the per-dimension paddings.</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257310&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
-<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd9542572b0&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aafe0&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aaf80&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
 <li><p><strong>with_bias</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to include a bias term, default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>data_format</strong> (default: <code class="docutils literal notranslate"><span class="pre">'NHWC'</span></code>) – NHWC” or “NCHW”. Defaults to “NHWC”.</p></li>
 <li><p><strong>dilations</strong> (default: <code class="docutils literal notranslate"><span class="pre">1</span></code>) – The dilation factor for each dimension of input. (Default value = 1)</p></li>
@@ -1565,8 +1565,8 @@
 <li><p><strong>strides</strong> – The stride of the sliding window for each dimension of input.</p></li>
 <li><p><strong>padding</strong> – SAME” or “VALID” indicating the algorithm, or
 list indicating the per-dimension paddings.</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257250&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
-<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd9542571f0&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aaf20&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aaec0&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
 <li><p><strong>with_bias</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to include a bias term, default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>output_shape</strong> (default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – Shape of the output (Default value = None)</p></li>
 <li><p><strong>data_format</strong> (default: <code class="docutils literal notranslate"><span class="pre">'NHWC'</span></code>) – NHWC” or “NCHW”. Defaults to “NHWC”.</p></li>
@@ -1604,8 +1604,8 @@
 <li><p><strong>strides</strong> – The stride of the sliding window for each dimension of input.</p></li>
 <li><p><strong>padding</strong> – SAME” or “VALID” indicating the algorithm, or
 list indicating the per-dimension paddings.</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd9542570d0&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
-<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257070&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aada0&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aad40&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
 <li><p><strong>with_bias</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to include a bias term, default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>data_format</strong> (default: <code class="docutils literal notranslate"><span class="pre">'NDHWC'</span></code>) – NDHWC” or “NCDHW”. Defaults to “NDHWC”.</p></li>
 <li><p><strong>dilations</strong> (default: <code class="docutils literal notranslate"><span class="pre">1</span></code>) – The dilation factor for each dimension of input. (Default value = 1)</p></li>
@@ -1642,8 +1642,8 @@
 <li><p><strong>strides</strong> – The stride of the sliding window for each dimension of input.</p></li>
 <li><p><strong>padding</strong> – SAME” or “VALID” indicating the algorithm, or
 list indicating the per-dimension paddings.</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257010&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
-<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954256fb0&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aace0&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aac80&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
 <li><p><strong>with_bias</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to include a bias term, default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>output_shape</strong> (default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – Shape of the output (Default value = None)</p></li>
 <li><p><strong>data_format</strong> (default: <code class="docutils literal notranslate"><span class="pre">'NDHWC'</span></code>) – NDHWC” or “NCDHW”. Defaults to “NDHWC”.</p></li>
@@ -1706,8 +1706,8 @@
 <li><p><strong>strides</strong> – The stride of the sliding window for each dimension of input.</p></li>
 <li><p><strong>padding</strong> – SAME” or “VALID” indicating the algorithm, or
 list indicating the per-dimension paddings.</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257190&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
-<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257130&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aae60&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aae00&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
 <li><p><strong>with_bias</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to include a bias term, default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>data_format</strong> (default: <code class="docutils literal notranslate"><span class="pre">'NHWC'</span></code>) – NHWC” or “NCHW”. Defaults to “NHWC”.</p></li>
 <li><p><strong>dilations</strong> (default: <code class="docutils literal notranslate"><span class="pre">1</span></code>) – The dilation factor for each dimension of input. (Default value = 1)</p></li>
@@ -1894,7 +1894,7 @@
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>input_channels</strong> – Number of input channels for the layer</p></li>
 <li><p><strong>output_channels</strong> – Number of output channels for the layer</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954256f50&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89aac20&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
 <li><p><strong>num_layers</strong> (default: <code class="docutils literal notranslate"><span class="pre">1</span></code>) – Number of lstm cells in the lstm layer, default is <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>return_sequence</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to return the entire output sequence, or
 just the latest timestep.
@@ -1953,8 +1953,8 @@
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>input_channels</strong> – Number of input channels for the layer.</p></li>
 <li><p><strong>output_channels</strong> – Number of output channels for the layer.</p></li>
-<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd954257550&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
-<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fd9542574f0&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
+<li><p><strong>weight_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.GlorotUniform</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89ab220&gt;</span></code>) – Initializer for the weights. Default is GlorotUniform.</p></li>
+<li><p><strong>bias_initializer</strong> (default: <code class="docutils literal notranslate"><span class="pre">&lt;ivy.stateful.initializers.Zeros</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f95b89ab1c0&gt;</span></code>) – Initializer for the bias. Default is Zeros.</p></li>
 <li><p><strong>with_bias</strong> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) – Whether or not to include a bias term, default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>device</strong> (default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – device on which to create the layer’s variables ‘cuda:0’, ‘cuda:1’, ‘cpu’
 etc. Default is cpu.</p></li>
diff --git a/index.html b/index.html
index 2183879e2..2c341cde5 100644
--- a/index.html
+++ b/index.html
@@ -1289,33 +1289,45 @@
                 <article class="bd-article">
                   
   <div style="display: block;" align="center">
-    <img class="dark-light" width="50%" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.svg"/>
+    <a href="https://ivy.dev/">
+        <img class="dark-light" width="50%" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.svg"/>
+    </a>
 </div>
 <hr class="docutils" />
 <table align="center">
   <tr>
     <td align="center">
-      <img class="dark-light" width="75" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/website.svg" alt="Website">
+      <a href="https://ivy.dev/">
+          <img class="dark-light" width="75" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/website.svg" alt="Website">
+      </a>
       <br>
       <a href="https://ivy.dev/" style="text-decoration: none;">Website</a>
     </td>
     <td align="center">
-      <img class="dark-light" width="70" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/docs.svg" alt="Docs">
+      <a href="https://ivy.dev/docs">
+          <img class="dark-light" width="70" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/docs.svg" alt="Docs">
+      </a>
       <br>
       <a href="https://ivy.dev/docs" style="text-decoration: none;">Docs</a>
     </td>
     <td align="center">
-      <img class="dark-light" width="75" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/demos.svg" alt="Demos">
+      <a href="https://ivy.dev/docs/demos">
+          <img class="dark-light" width="75" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/demos.svg" alt="Demos">
+      </a>
       <br>
       <a href="https://ivy.dev/docs/demos" style="text-decoration: none;">Demos</a>
     </td>
     <td align="center">
-      <img class="dark-light" width="75" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/design.svg" alt="Design">
+      <a href="https://ivy.dev/docs/overview/design">
+          <img class="dark-light" width="75" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/design.svg" alt="Design">
+      </a>
       <br>
       <a href="https://ivy.dev/docs/overview/design" style="text-decoration: none;">Design</a>
     </td>
     <td align="center">
-      <img class="dark-light" width="75" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/faq.svg" alt="FAQ">
+      <a href="https://ivy.dev/docs/overview/faq">
+          <img class="dark-light" width="75" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/faq.svg" alt="FAQ">
+      </a>
       <br>
       <a href="https://ivy.dev/docs/overview/faq" style="text-decoration: none;">FAQ</a>
     </td>
diff --git a/overview/related_work/ivy_vs_onnx.html b/overview/related_work/ivy_vs_onnx.html
index c2214657a..a4a14bff9 100644
--- a/overview/related_work/ivy_vs_onnx.html
+++ b/overview/related_work/ivy_vs_onnx.html
@@ -1332,7 +1332,7 @@ <h2>ONNX <a class="dark-light reference internal" href="https://raw.githubuserco
 signal processing, and advanced linear algebra.</p>
 </section>
 <section id="ivy">
-<h2><a class="dark-light reference internal" href="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.svg"><img alt="ivy" class="dark-light" height="26pt" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.svg" /></a><a class="headerlink" href="#ivy" title="Link to this heading">#</a></h2>
+<h2><a class="dark-light reference internal" href="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.png"><img alt="ivy" class="dark-light" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.png" style="height: 40pt;" /></a><a class="headerlink" href="#ivy" title="Link to this heading">#</a></h2>
 <p>The goal of Ivy is to be a comprehensive ML code conversion tool for all aspects of ML development, rather than solely focusing on deployment.
 Ivy’s transpiler uses a source code-to-source code approach to conversion, allowing any ML code to be converted. This includes models, functions, tools,
 and entire libraries or codebases, providing a holistic solution for ML framework interoperability. Exchange formats like ONNX primarily work with deep
diff --git a/searchindex.js b/searchindex.js
index 2383fe786..3fc38b657 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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5, 6, 7, 12, 13, 20, 21, 22, 23, 24, 25, 26, 36, 45, 48, 59, 60, 843, 858, 865, 868, 884], "restart": [2, 4, 5, 6, 7, 12, 13, 22, 23, 24, 59, 60, 840, 855], "git": [2, 4, 5, 6, 7, 12, 13, 22, 23, 45, 59, 60, 61, 62, 833, 835, 838, 840, 841, 844, 847, 849, 855, 856, 865, 873], "clone": [2, 4, 5, 12, 13, 22, 23, 45, 59, 61, 62, 833, 835, 841, 855, 873], "http": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 24, 25, 26, 30, 38, 40, 41, 45, 46, 59, 60, 61, 62, 63, 64, 70, 71, 93, 94, 96, 162, 170, 258, 268, 269, 284, 343, 350, 351, 384, 387, 390, 393, 402, 434, 508, 538, 631, 632, 646, 647, 649, 652, 654, 656, 664, 702, 703, 734, 784, 833, 835, 840, 841, 844, 847, 849, 850, 853, 855, 873, 879], "github": [2, 4, 5, 6, 7, 12, 13, 20, 21, 22, 23, 25, 26, 45, 59, 60, 61, 62, 63, 833, 835, 836, 838, 841, 842, 844, 847, 849, 850, 852, 853, 855, 856, 864, 865, 873], "com": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 25, 26, 30, 45, 59, 60, 61, 62, 63, 833, 835, 840, 841, 844, 847, 849, 850, 855, 873], "llc": [2, 4, 5, 12, 13, 22, 23, 45, 59, 60, 61, 62, 833, 873], "model": [2, 3, 4, 5, 16, 17, 27, 32, 33, 34, 62, 64, 255, 288, 392, 468, 649, 809, 813, 814, 818, 832, 833, 871, 874, 876, 877, 879, 880, 884, 885, 886], "depth": [2, 4, 5, 8, 9, 12, 13, 22, 23, 60, 67, 71, 75, 90, 94, 98, 156, 390, 393, 426, 486, 561, 573, 646, 651, 653, 671, 672, 841, 849, 870, 873], "repositori": [2, 4, 5, 12, 13, 22, 23, 835, 839, 840, 841, 843, 844, 847, 855, 864], "cd": [2, 4, 5, 12, 13, 22, 23, 45, 62, 833, 835, 840, 841, 855, 873], "acceler": [3, 32, 850, 862, 877], "convert": [3, 12, 13, 15, 16, 17, 20, 21, 25, 26, 27, 28, 30, 32, 35, 37, 40, 41, 43, 44, 45, 46, 47, 49, 51, 59, 62, 64, 66, 67, 70, 88, 89, 90, 93, 111, 141, 142, 155, 165, 166, 208, 209, 210, 211, 222, 230, 234, 254, 294, 393, 398, 477, 478, 479, 529, 594, 612, 614, 615, 616, 618, 646, 647, 648, 649, 651, 654, 658, 712, 739, 750, 751, 793, 822, 827, 839, 845, 846, 859, 860, 862, 865, 867, 870, 871, 872, 874, 875, 877, 878, 879, 881, 886], "faster": [3, 4, 5, 16, 17, 20, 21, 25, 26, 27, 32, 45, 46, 62, 64, 71, 76, 94, 99, 391, 464, 654, 704, 835, 838, 847, 874], "infer": [3, 8, 9, 10, 11, 16, 17, 20, 21, 24, 25, 26, 27, 32, 36, 48, 50, 51, 60, 62, 64, 67, 71, 72, 75, 78, 90, 94, 95, 98, 101, 140, 142, 146, 150, 151, 155, 158, 164, 173, 174, 175, 176, 177, 327, 328, 390, 393, 397, 426, 512, 526, 572, 606, 607, 646, 647, 651, 653, 656, 676, 726, 822, 823, 843, 846, 850, 851, 865, 870, 879, 886], "finetun": [3, 32, 59], "project": [3, 22, 23, 25, 26, 32, 37, 38, 40, 41, 45, 46, 49, 112, 653, 680, 812, 833, 835, 836, 839, 840, 841, 842, 845, 846, 847, 865, 872, 877, 879, 884], "resnet": [3, 8, 9, 25, 26, 32, 45, 879, 880], "video": [4, 12, 20, 22, 25, 28, 30, 34, 35, 36, 37, 38, 39, 40, 41, 46, 833, 834, 839, 840, 841, 844, 845, 846, 848, 849, 850, 851, 852, 853, 854, 856, 857, 858, 859, 860, 861, 862, 863, 865, 866, 868, 873], "tutori": [4, 8, 9, 10, 11, 12, 20, 22, 24, 25, 28, 30, 34, 35, 36, 37, 38, 39, 40, 41, 46, 833, 841, 862, 873], "written": [4, 5, 6, 7, 8, 9, 24, 34, 44, 45, 46, 59, 72, 393, 488, 840, 844, 845, 853, 856, 857, 861, 862, 866, 870, 871, 872, 879, 881, 884], "imag": [4, 5, 8, 9, 10, 11, 20, 21, 25, 26, 28, 40, 45, 46, 59, 60, 61, 62, 63, 64, 71, 75, 93, 94, 98, 116, 143, 235, 236, 237, 238, 241, 244, 253, 256, 258, 260, 269, 270, 271, 276, 278, 291, 298, 299, 301, 302, 306, 390, 409, 410, 426, 427, 428, 430, 561, 646, 649, 651, 653, 655, 666, 667, 668, 669, 670, 673, 674, 675, 716, 812, 833, 840, 855, 868, 873, 879, 880, 884], "classif": [4, 5, 22, 23, 27, 59, 884], "three": [4, 5, 6, 7, 32, 38, 50, 51, 61, 71, 154, 327, 384, 393, 479, 646, 840, 841, 848, 849, 850, 852, 862, 865, 868, 871, 885], "major": [4, 5, 6, 7, 661, 767, 850, 851, 863, 865, 872, 884], "ml": [4, 5, 6, 7, 8, 9, 24, 32, 33, 34, 35, 36, 37, 38, 40, 41, 45, 46, 47, 48, 49, 50, 51, 52, 59, 61, 64, 833, 834, 838, 862, 871, 875, 877, 879, 883, 884, 886], "framework": [4, 5, 6, 7, 10, 11, 15, 16, 17, 28, 30, 34, 35, 36, 37, 38, 40, 41, 46, 47, 48, 49, 50, 52, 59, 61, 63, 66, 72, 185, 207, 217, 220, 231, 559, 575, 579, 611, 614, 647, 648, 651, 658, 740, 791, 793, 797, 804, 809, 816, 822, 823, 836, 837, 839, 840, 843, 844, 845, 846, 847, 849, 850, 851, 852, 854, 855, 857, 858, 859, 861, 862, 865, 866, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 885, 886], "sinc": [4, 5, 12, 13, 22, 23, 24, 40, 41, 45, 46, 59, 61, 71, 94, 112, 387, 835, 840, 841, 844, 845, 846, 847, 848, 849, 850, 851, 854, 861, 862, 872, 884], "automat": [4, 5, 12, 13, 16, 17, 22, 23, 24, 41, 45, 46, 51, 839, 840, 841, 843, 846, 847, 849, 850, 856, 858, 861, 865, 868, 874, 876, 884], "sure": [4, 5, 12, 13, 20, 21, 22, 23, 24, 25, 26, 27, 45, 59, 836, 839, 840, 841, 844, 849, 854, 855, 862, 863, 865, 868], "enabl": [4, 5, 6, 7, 8, 9, 12, 13, 20, 21, 22, 23, 24, 25, 26, 27, 38, 41, 60, 71, 76, 88, 99, 117, 390, 392, 413, 471, 596, 640, 651, 652, 654, 697, 814, 832, 833, 840, 841, 842, 845, 848, 850, 858, 859, 860, 861, 862, 865, 866, 870, 871, 872, 874, 877, 880, 884, 885, 886], "dm": [4, 5, 6, 7, 12, 13, 20, 21, 25, 26, 45, 46, 57, 59], "haiku": [4, 5, 6, 7, 12, 13, 20, 21, 25, 26, 41, 45, 46, 57, 59, 63, 809, 833, 879, 884], "exit": [4, 12, 22, 24, 45, 46, 851], "download": [4, 5, 8, 9, 10, 11, 22, 23, 24, 28, 30, 45, 46, 60, 61, 64, 835, 840, 847, 865, 879, 880], "imagenet": [4, 5, 8, 9, 24, 30, 60, 62, 833], "class": [4, 5, 8, 9, 10, 11, 12, 13, 15, 22, 23, 24, 27, 28, 30, 34, 44, 45, 46, 57, 58, 59, 60, 61, 62, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 121, 149, 158, 164, 180, 183, 196, 198, 199, 258, 295, 353, 375, 387, 401, 402, 410, 411, 444, 544, 545, 552, 561, 565, 578, 588, 611, 646, 647, 648, 649, 651, 653, 654, 655, 658, 659, 674, 679, 683, 689, 699, 703, 704, 706, 713, 732, 739, 750, 757, 772, 779, 783, 784, 793, 794, 801, 802, 803, 804, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 821, 822, 825, 827, 832, 839, 846, 847, 848, 850, 851, 852, 853, 857, 859, 860, 863, 864, 865, 868, 870, 871, 874, 878, 879, 880, 881, 884, 885], "wget": [4, 5, 8, 9, 12, 13, 22, 23, 59, 60, 63, 840], "raw": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 25, 26, 40, 45, 46, 59, 62, 63, 88, 833, 853, 879, 885], "githubusercont": [4, 5, 8, 9, 12, 13, 22, 23, 59, 63], "hub": [4, 5, 8, 9, 12, 13, 22, 23, 59, 62, 64], "master": [4, 5, 12, 13, 22, 23, 35, 36, 37, 47, 48, 49, 50, 51, 52, 59, 61, 62, 63, 836, 849, 884], "imagenet_class": [4, 5, 22, 23], "categori": [4, 5, 8, 9, 22, 23, 839, 844, 845, 848, 850, 854, 862, 866], "strip": [4, 5, 22, 23, 36, 48], "readlin": [4, 5, 22, 23, 60], "cat": [4, 5, 10, 11, 22, 23, 60, 863, 868, 870, 871, 879, 880], "jpg": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 25, 26, 40, 45, 46, 61, 62, 833, 879], "filenam": [4, 5, 12, 13, 22, 23, 24, 45, 46, 59, 61, 64, 72, 814, 821], "import": [4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 28, 30, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 59, 60, 62, 63, 64, 71, 82, 86, 90, 94, 109, 209, 210, 214, 226, 322, 402, 538, 573, 589, 648, 651, 655, 657, 662, 716, 736, 737, 772, 804, 818, 822, 823, 833, 838, 839, 840, 841, 842, 844, 845, 846, 847, 848, 850, 851, 852, 853, 856, 859, 860, 861, 862, 863, 864, 865, 866, 870, 871, 872, 873, 879, 880, 882, 883, 884], "devic": [4, 5, 8, 9, 10, 11, 12, 16, 17, 20, 21, 22, 23, 24, 25, 26, 60, 61, 64, 67, 71, 80, 88, 90, 94, 103, 116, 119, 120, 121, 140, 141, 142, 145, 146, 147, 150, 151, 152, 153, 155, 156, 157, 158, 160, 161, 162, 163, 164, 208, 209, 210, 211, 212, 213, 214, 215, 216, 221, 222, 223, 224, 226, 227, 228, 229, 230, 232, 234, 327, 328, 343, 344, 384, 397, 487, 524, 525, 527, 528, 552, 566, 567, 646, 651, 660, 758, 759, 760, 761, 791, 793, 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22, 23, 38, 40, 41, 45, 46, 64], "displai": [4, 5, 12, 13, 22, 23, 24, 40, 45, 46, 59, 60, 61, 63, 64, 840, 847, 849, 854, 865], "end": [4, 5, 12, 13, 24, 59, 60, 71, 94, 140, 243, 299, 368, 387, 390, 392, 393, 438, 467, 489, 499, 502, 503, 646, 649, 828, 840, 841, 846, 849, 855, 861, 866, 868, 872, 886], "set_default_devic": [4, 6, 8, 9, 12, 13, 20, 22, 24, 25, 232, 648, 851], "ivy_model": [4, 5, 6, 7, 12, 13, 22, 23, 62], "ivy_alexnet": [4, 5], "quick": [4, 5, 32, 46, 841, 843, 863], "trace_graph": [4, 5, 6, 7, 12, 13, 22, 23, 36, 37, 38, 39, 45, 46, 48, 49, 50, 51, 52, 53, 62, 814, 871, 878], "moment": [4, 5, 71, 73, 94, 96, 391, 448, 631, 632, 637, 652, 816, 832, 839, 846, 872, 879, 880], "cost": [4, 5, 73, 96, 631, 632, 635, 637, 638, 639, 652, 657, 735, 736, 737, 828, 850, 868], "arg": [4, 5, 8, 9, 12, 13, 16, 17, 18, 20, 21, 22, 23, 24, 28, 30, 38, 39, 41, 45, 46, 50, 51, 52, 63, 66, 88, 110, 120, 136, 218, 228, 617, 645, 646, 648, 651, 791, 793, 808, 809, 812, 813, 814, 819, 822, 827, 832, 833, 845, 850, 851, 854, 860, 861, 862, 868, 870, 879, 880, 882], "asarrai": [4, 5, 6, 7, 12, 13, 20, 21, 22, 23, 60, 67, 71, 72, 83, 90, 94, 95, 106, 141, 400, 530, 531, 561, 572, 576, 577, 607, 608, 609, 646, 651, 653, 662, 663, 667, 770, 774, 854, 859, 862, 863], "cuda": [4, 5, 6, 8, 9, 10, 11, 12, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 34, 45, 60, 61, 64, 67, 71, 80, 90, 94, 103, 152, 153, 156, 208, 209, 210, 226, 397, 524, 525, 527, 528, 646, 648, 654, 660, 705, 758, 759, 760, 761, 811, 812, 813, 814, 815, 816, 817, 832, 870, 871, 872, 874], "output": [4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 18, 22, 23, 24, 34, 40, 41, 43, 45, 46, 58, 59, 60, 62, 65, 67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 116, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 140, 141, 142, 143, 144, 145, 146, 147, 148, 150, 151, 152, 153, 154, 156, 157, 158, 159, 160, 161, 163, 164, 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868, 870, 871, 879, 880], "ll": [4, 5, 8, 9, 10, 11, 12, 13, 16, 17, 20, 21, 24, 25, 26, 34, 35, 36, 37, 38, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 60, 833, 834, 836, 837, 839, 840, 841, 842, 847, 852, 855, 856, 860, 861, 879, 880], "try": [4, 5, 8, 9, 10, 11, 24, 35, 47, 57, 60, 64, 88, 617, 651, 811, 822, 833, 839, 840, 841, 844, 845, 848, 849, 850, 854, 856, 861, 863, 870, 872, 882], "tf": [4, 5, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 24, 25, 26, 28, 30, 35, 38, 39, 41, 43, 44, 45, 46, 47, 48, 50, 52, 57, 62, 63, 809, 818, 833, 845, 850, 851, 857, 861, 862, 865, 866, 868, 870, 871, 872, 874, 879, 880, 882, 884], "onc": [4, 5, 8, 9, 12, 13, 45, 46, 57, 59, 76, 80, 99, 103, 228, 391, 444, 648, 654, 660, 689, 690, 691, 704, 758, 839, 840, 841, 848, 849, 850, 851, 852, 855, 856, 861, 862, 865, 868, 870, 872, 880], "set": [4, 5, 10, 11, 16, 17, 28, 30, 36, 45, 46, 48, 51, 59, 60, 61, 62, 63, 66, 71, 72, 75, 76, 81, 83, 84, 88, 94, 95, 98, 99, 104, 106, 107, 129, 132, 139, 160, 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21, 796, 844, 863, 865], "perf_count": [4, 5, 16, 17, 18, 20, 21], "raw_logit": [4, 5], "latenc": [4, 5, 20, 21], "nn": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 30, 41, 44, 45, 46, 59, 63, 154, 646, 818, 833, 858, 863, 868, 870, 871, 879, 885], "direct": [4, 5, 71, 94, 356, 363, 367, 372, 376, 387, 390, 393, 424, 435, 490, 491, 506, 663, 776, 839, 845, 847, 862, 868, 870, 883, 884, 885], "tolist": [4, 5], "652289830999962": [4, 5], "int32": [4, 5, 57, 59, 68, 71, 72, 80, 81, 84, 91, 94, 95, 103, 104, 147, 152, 156, 158, 164, 167, 170, 172, 174, 176, 178, 181, 183, 184, 188, 191, 195, 199, 203, 205, 223, 250, 286, 287, 398, 402, 529, 539, 540, 541, 569, 578, 615, 646, 647, 648, 649, 651, 660, 661, 664, 759, 760, 761, 765, 777, 778, 783, 785, 796, 797, 850, 862, 865, 870], "6477362": [4, 5], "29496726": [4, 5], "04526032": [4, 5], "As": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 24, 25, 26, 27, 28, 30, 36, 40, 41, 45, 46, 48, 51, 57, 58, 82, 86, 109, 654, 662, 702, 769, 770, 771, 772, 837, 839, 840, 841, 842, 845, 847, 848, 849, 850, 851, 854, 855, 856, 857, 858, 861, 862, 863, 864, 865, 868, 872, 879, 880, 882, 884], "ident": [4, 5, 8, 9, 16, 17, 27, 41, 43, 60, 62, 76, 88, 147, 216, 571, 597, 646, 648, 651, 654, 658, 692, 696, 751, 812, 818, 848, 858, 859, 862, 863, 866, 868, 872, 874], "had": [4, 5, 848, 849, 861, 866, 870, 871, 884, 885], "postprocess": [4, 5], "routin": [4, 5, 849, 861, 862, 868, 872, 884], "feed": [4, 5, 228, 648, 880, 884, 885], "carefulli": [4, 5, 293, 649, 811, 862], "rewrit": [4, 5], "easili": [4, 5, 40, 45, 46, 57, 840, 845, 849, 855, 862, 865, 868, 871, 872, 877, 884], "quickest": [4, 5], "particular": [4, 5, 45, 46, 283, 649, 797, 840, 841, 844, 846, 849, 850, 852, 859, 861, 862, 865, 866, 884], "again": [4, 5, 12, 13, 37, 38, 48, 49, 50, 51, 654, 702, 841, 845, 846, 847, 848, 852, 854, 856, 861, 862, 865, 866, 868, 870, 872], "speed": [4, 5, 20, 21, 25, 26, 27, 45, 46, 59, 64, 72, 95, 585, 651, 865], "repeat": [4, 5, 6, 7, 37, 49, 71, 72, 78, 94, 95, 101, 390, 393, 402, 419, 424, 488, 538, 563, 651, 656, 657, 732, 736, 737, 827, 841, 845, 846, 852, 853, 861, 865], "previou": [4, 5, 27, 36, 37, 38, 40, 48, 49, 50, 52, 73, 94, 96, 202, 203, 204, 205, 206, 379, 389, 390, 436, 618, 620, 621, 622, 623, 625, 626, 628, 632, 637, 647, 651, 652, 811, 831, 840, 841, 844, 846, 849, 851, 857, 862, 865, 868, 872], "trace": [4, 5, 6, 7, 8, 9, 12, 13, 20, 21, 22, 23, 24, 25, 26, 32, 33, 37, 40, 45, 48, 50, 51, 63, 72, 76, 88, 95, 99, 580, 581, 584, 595, 604, 619, 627, 651, 654, 793, 804, 814, 816, 832, 833, 844, 848, 850, 862, 867, 868, 871, 872, 878, 879, 880, 885], "026875037000081647": [4, 5], "overrid": [4, 5, 12, 13, 51, 60, 67, 71, 90, 94, 156, 402, 538, 646, 845, 847], "prealloc": [4, 5, 12, 13], "temporari": [4, 5, 12, 13, 605, 628, 651, 828, 850, 867], "fix": [4, 5, 12, 13, 61, 71, 94, 111, 112, 387, 390, 391, 436, 466, 653, 680, 833, 837, 840, 841, 844, 850, 856, 865, 866], "until": [4, 5, 12, 13, 828, 841, 861, 870, 872, 880], "o": [4, 5, 12, 13, 24, 58, 59, 60, 61, 63, 588, 651, 653, 680, 833, 840, 843, 849, 871, 873], "environ": [4, 5, 12, 13, 25, 26, 38, 40, 41, 60, 63, 833, 834, 841, 873, 876, 884, 886], "xla_python_client_alloc": [4, 5, 12, 13], "platform": [4, 5, 8, 9, 12, 13, 24, 27, 38, 41, 835, 838, 840, 847, 886], "jit": [4, 5, 20, 21, 25, 26, 45, 48, 871, 872, 880, 884], "img_jax": [4, 5, 12, 13], "device_put": [4, 5, 20, 21], "warm": [4, 5], "_": [4, 5, 16, 17, 18, 20, 21, 25, 26, 27, 45, 58, 59, 70, 71, 88, 93, 94, 96, 112, 170, 258, 260, 268, 269, 284, 350, 351, 387, 390, 393, 402, 434, 463, 466, 508, 538, 561, 631, 632, 647, 649, 651, 652, 654, 656, 658, 664, 702, 703, 705, 734, 745, 784, 841, 849, 850, 853, 861, 865], "0022192720000475674": [4, 5], "64773613": [4, 5], "29496723": [4, 5], "exact": [4, 5, 71, 87, 88, 124, 390, 392, 426, 431, 471, 472, 662, 769, 771, 798, 808, 840, 841, 844, 852, 871], "note": [4, 5, 8, 9, 12, 13, 24, 27, 45, 46, 51, 60, 61, 62, 71, 72, 76, 78, 82, 94, 99, 101, 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48, 62, 71, 94, 390, 393, 426, 491, 833, 840, 865, 874, 884], "itself": [4, 5, 10, 11, 38, 50, 70, 111, 289, 551, 617, 649, 651, 658, 750, 828, 837, 840, 841, 844, 847, 848, 849, 850, 851, 854, 855, 856, 861, 862, 872, 880, 884, 885], "version": [4, 5, 8, 9, 16, 17, 27, 40, 41, 48, 59, 60, 61, 64, 65, 71, 94, 111, 124, 306, 355, 357, 387, 402, 543, 548, 630, 649, 651, 654, 690, 691, 793, 818, 822, 823, 833, 840, 841, 847, 849, 850, 853, 861, 863, 870, 877, 879], "004749261999904775": [4, 5], "7245": [4, 5], "1394": [4, 5], "0587": [4, 5], "promis": [4, 5, 10, 11], "sourc": [4, 5, 10, 11, 14, 15, 16, 17, 18, 19, 22, 23, 30, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 51, 52, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 121, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 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"modes_to_trac": 30, "1280": [30, 561, 651, 833], "welcom": [32, 60, 833, 834, 840, 841, 842, 864], "varieti": [32, 844, 849, 850, 851, 865, 867], "organ": [32, 845, 848, 858, 862, 864, 866, 874, 876, 877], "main": [32, 46, 67, 71, 76, 94, 99, 147, 160, 161, 162, 328, 343, 344, 384, 391, 393, 442, 488, 646, 654, 687, 688, 708, 833, 836, 839, 840, 841, 842, 844, 847, 848, 855, 859, 861, 884, 885], "exactli": [32, 36, 43, 48, 57, 58, 62, 305, 649, 839, 848, 849, 850, 851, 852, 854, 865, 868], "rush": 32, "jump": [32, 863], "straight": [32, 833, 849, 862, 865], "quickstart": [32, 833], "introduct": [32, 34, 41, 45, 46, 884], "point": [32, 41, 68, 70, 71, 76, 80, 82, 84, 91, 93, 94, 99, 103, 107, 140, 141, 142, 145, 147, 150, 157, 158, 163, 167, 180, 184, 188, 195, 235, 236, 237, 238, 240, 241, 242, 243, 244, 251, 252, 253, 255, 256, 258, 260, 261, 262, 268, 269, 270, 271, 276, 277, 278, 279, 280, 288, 290, 291, 293, 295, 297, 298, 299, 300, 301, 302, 303, 305, 306, 307, 308, 309, 327, 328, 330, 350, 351, 368, 369, 372, 374, 384, 387, 390, 391, 392, 397, 402, 405, 414, 415, 416, 434, 444, 464, 468, 524, 525, 526, 527, 528, 538, 539, 540, 548, 644, 646, 647, 649, 654, 660, 661, 662, 663, 664, 684, 686, 689, 690, 691, 693, 695, 696, 697, 700, 701, 702, 703, 704, 705, 706, 708, 711, 760, 761, 767, 769, 770, 771, 772, 775, 777, 778, 780, 781, 782, 783, 784, 785, 786, 822, 823, 832, 837, 839, 840, 841, 844, 845, 847, 849, 850, 852, 853, 855, 857, 861, 862, 865, 866, 868, 870, 880], "showcas": [32, 833], "real": [32, 40, 70, 71, 84, 93, 94, 107, 116, 126, 129, 132, 143, 157, 158, 235, 236, 237, 238, 240, 241, 242, 243, 244, 253, 255, 256, 258, 260, 262, 266, 267, 269, 270, 271, 272, 274, 275, 276, 277, 278, 279, 280, 285, 288, 290, 291, 293, 297, 298, 299, 301, 302, 303, 304, 305, 306, 308, 309, 350, 351, 357, 358, 359, 369, 387, 390, 391, 413, 434, 435, 444, 445, 643, 646, 649, 654, 661, 664, 689, 690, 691, 695, 702, 704, 705, 708, 711, 767, 780, 782, 783, 784, 785, 848], "world": [32, 40, 841], "beginn": [32, 834, 884], "got": [32, 57, 854], "cover": [32, 45, 71, 94, 390, 427, 428, 429, 839, 844, 845, 847, 850, 852, 853, 858, 859, 865, 868], "lazi": [32, 33, 36, 39, 48, 51, 52, 63], "familiar": [34, 839, 840], "concept": 34, "roundup": [34, 877], "indep": [34, 45], "proof": [34, 45], "delv": [34, 46, 833], "theori": [34, 835, 847], "esenti": [34, 45], "abstract": [34, 45, 46, 811, 816, 833, 848, 850, 861, 862, 865, 868, 884], "quirk": [34, 45], "perk": [34, 45, 833, 845, 848], "under": [34, 45, 46, 71, 392, 471, 472, 827, 839, 840, 843, 844, 851, 852, 853, 856, 862, 863, 865, 868, 869, 871, 872, 879, 880, 884], "hood": [34, 45, 46, 843, 851, 852, 856, 862, 865, 868, 869, 871, 879, 880], "appropi": 34, "string": [34, 45, 46, 61, 71, 72, 75, 88, 94, 98, 165, 166, 178, 185, 207, 208, 209, 210, 211, 213, 222, 229, 230, 234, 390, 391, 393, 433, 437, 445, 499, 511, 540, 559, 647, 648, 651, 653, 654, 666, 667, 668, 669, 671, 673, 675, 691, 791, 793, 797, 827, 828, 846, 847, 849, 850, 851, 854, 862, 870], "simplest": [34, 840, 852, 865, 868], "interact": [34, 45, 60, 63, 839, 884], "submodul": [34, 45, 59, 61, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 839, 840, 841, 844, 847, 849, 851, 855, 858, 859, 865, 870, 874], "likewis": [34, 39, 45, 52, 841, 848, 850, 853, 857, 858, 862, 868, 879, 880], "nativearrai": [34, 45, 46, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 82, 84, 87, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 116, 120, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 136, 137, 139, 141, 142, 143, 144, 146, 151, 152, 153, 154, 155, 156, 158, 160, 161, 164, 167, 168, 169, 170, 173, 174, 175, 176, 177, 178, 180, 183, 186, 187, 188, 190, 192, 194, 195, 201, 211, 212, 228, 229, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 328, 329, 332, 333, 337, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 382, 384, 387, 388, 390, 391, 392, 393, 396, 397, 398, 400, 402, 404, 405, 406, 407, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 423, 424, 426, 427, 428, 429, 430, 432, 433, 434, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 455, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 482, 483, 484, 485, 487, 488, 489, 490, 491, 493, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 506, 507, 508, 509, 510, 512, 513, 514, 515, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 538, 539, 540, 541, 542, 550, 553, 554, 556, 557, 561, 562, 563, 565, 568, 569, 570, 571, 572, 574, 576, 577, 578, 581, 584, 585, 587, 592, 593, 594, 597, 606, 607, 608, 609, 610, 611, 613, 615, 616, 618, 629, 631, 632, 633, 635, 637, 638, 639, 640, 641, 643, 645, 646, 647, 648, 649, 651, 652, 653, 654, 655, 656, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 679, 680, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 708, 709, 710, 711, 712, 713, 714, 715, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 738, 739, 740, 741, 745, 746, 747, 750, 755, 756, 757, 758, 759, 760, 761, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 817, 845, 848, 852, 854, 857, 858, 859, 861, 862, 866, 867, 870, 874], "alia": [34, 45, 350, 351, 387, 644, 839, 862, 882], "lastli": [34, 45, 845], "subclass": [34, 45, 46, 818, 859, 862, 868, 879], "dict": [34, 45, 46, 59, 63, 66, 72, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 117, 124, 125, 126, 127, 128, 129, 130, 131, 132, 137, 139, 149, 151, 156, 158, 164, 168, 170, 181, 182, 183, 187, 188, 195, 211, 214, 215, 229, 235, 236, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 260, 261, 262, 266, 267, 269, 270, 271, 272, 274, 275, 276, 277, 278, 279, 280, 282, 283, 284, 285, 286, 287, 288, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 308, 309, 310, 311, 312, 313, 314, 317, 318, 319, 320, 321, 322, 324, 325, 326, 328, 340, 349, 350, 351, 352, 353, 355, 357, 365, 366, 372, 374, 376, 377, 378, 384, 393, 413, 414, 415, 416, 434, 467, 468, 469, 470, 471, 472, 473, 474, 477, 478, 479, 483, 484, 499, 506, 508, 509, 510, 512, 517, 519, 520, 521, 523, 525, 538, 539, 540, 541, 550, 551, 553, 554, 556, 557, 561, 562, 563, 564, 565, 566, 567, 568, 569, 572, 574, 576, 577, 578, 580, 581, 584, 588, 592, 593, 607, 608, 609, 611, 613, 615, 616, 629, 641, 645, 647, 648, 651, 658, 667, 668, 669, 670, 676, 677, 683, 684, 685, 690, 691, 692, 693, 694, 695, 697, 699, 701, 702, 708, 713, 714, 715, 719, 723, 726, 727, 728, 729, 730, 733, 734, 738, 739, 741, 744, 745, 746, 747, 749, 750, 751, 755, 756, 758, 759, 760, 761, 763, 766, 769, 770, 771, 772, 773, 777, 778, 781, 783, 784, 786, 787, 788, 793, 794, 809, 812, 814, 822, 828, 845, 848, 874, 879, 880, 882], "recurs": [34, 45, 46, 59, 61, 66, 88, 89, 181, 182, 214, 215, 391, 463, 566, 567, 573, 647, 648, 651, 658, 738, 739, 742, 748, 749, 750, 791, 840, 844, 847, 848, 855, 858, 861, 872], "fashion": [34, 798, 865, 879], "native_arrai": [34, 45, 46, 67, 68, 70, 90, 92, 93, 94, 95, 99, 106, 124, 127, 151, 154, 156, 158, 164, 167, 168, 169, 170, 178, 183, 190, 212, 221, 229, 245, 249, 254, 255, 256, 258, 262, 266, 274, 275, 283, 288, 291, 294, 297, 302, 350, 351, 378, 387, 392, 393, 473, 499, 500, 506, 510, 550, 553, 580, 581, 584, 615, 643, 646, 647, 648, 649, 651, 653, 654, 655, 656, 660, 661, 664, 665, 667, 668, 675, 683, 686, 690, 691, 696, 697, 701, 705, 706, 708, 711, 713, 715, 719, 726, 758, 767, 776, 782, 785, 787, 793, 803, 822, 837, 855, 863, 865], "data_class": [34, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 121, 410, 411, 561, 565, 704, 732], "low": [34, 45, 48, 64, 71, 75, 80, 94, 98, 103, 390, 433, 437, 653, 660, 666, 667, 668, 669, 671, 673, 675, 759, 761, 798, 848, 854, 861, 862, 868, 871, 884, 885], "c": [34, 45, 51, 60, 61, 67, 71, 72, 73, 75, 78, 84, 90, 91, 93, 94, 95, 96, 98, 99, 101, 105, 107, 111, 112, 130, 141, 142, 153, 156, 180, 183, 238, 249, 255, 256, 276, 277, 279, 288, 291, 299, 306, 390, 391, 393, 396, 402, 404, 405, 406, 407, 418, 423, 439, 441, 443, 444, 446, 458, 477, 478, 479, 489, 508, 512, 517, 518, 519, 522, 540, 553, 561, 562, 563, 564, 572, 576, 577, 607, 616, 631, 632, 635, 637, 638, 639, 643, 646, 647, 649, 651, 652, 653, 654, 656, 658, 661, 662, 664, 667, 668, 669, 670, 671, 672, 674, 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Ivy": [[61, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[61, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[61, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[61, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[61, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[61, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[61, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[61, "Plotting-the-training-metrics"]], "Save the trained Model": [[61, "Save-the-trained-Model"]], "0.1: Compile": [[48, "0.1:-Compile"]], "1.0: Lazy vs Eager": [[50, "1.0:-Lazy-vs-Eager"]], "Unify": [[50, "Unify"], [52, "Unify"], [51, "Unify"], [38, "Unify"]], "Compile": [[50, "Compile"], [52, "Compile"], [51, "Compile"]], "Transpile": [[50, "Transpile"], [52, "Transpile"], [51, "Transpile"], [38, "Transpile"]], "1.3: Dynamic vs Static": [[53, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[53, "Dynamic"]], "Static": [[53, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[53, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "2.0: Kornia": [[54, "2.0:-Kornia"]], "Conversions": [[66, "module-ivy.data_classes.array.conversions"], [89, "module-ivy.data_classes.container.conversions"]], "Data type": [[68, "module-ivy.data_classes.array.data_type"], [91, "module-ivy.data_classes.container.data_type"], [647, "data-type"], [385, "module-ivy.functional.ivy.experimental.data_type"]], "1.2: As a Decorator": [[52, "1.2:-As-a-Decorator"]], "Creation": [[67, "module-ivy.data_classes.array.creation"], [90, "module-ivy.data_classes.container.creation"], [646, "creation"], [384, "creation"]], "0.0: Unify": [[47, "0.0:-Unify"]], "Device": [[92, "module-ivy.data_classes.container.device"], [69, "module-ivy.data_classes.array.device"], [648, "device"], [386, "module-ivy.functional.ivy.experimental.device"]], "3.0: Perceiver": [[55, "3.0:-Perceiver"]], "Basic Operations with Ivy": [[57, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[57, "Installs-\ud83d\udcbe"], [58, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[57, "Imports-\ud83d\udec3"], [58, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[57, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[57, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[57, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[57, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[57, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[57, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[57, "Set-Backend-Framework"]], "Define Model": [[57, "Define-Model"], [58, "Define-Model"]], "Create Model": [[57, "Create-Model"]], "Create Optimizer": [[57, "Create-Optimizer"]], "Input and Target": [[57, "Input-and-Target"]], "Loss Function": [[57, "Loss-Function"]], "Training Loop": [[57, "Training-Loop"]], "Compilation of a Basic Function": [[58, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[58, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[58, "Function-compilation-\ud83d\udee0"]], "Set backend": [[58, "Set-backend"]], "Sample input": [[58, "Sample-input"]], "Define function to compile": [[58, "Define-function-to-compile"]], "Compile the function": [[58, "Compile-the-function"]], "Check results": [[58, "Check-results"], [58, "id1"]], "Compiling simple neural network \ud83e\udde0": [[58, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[58, "Create-model"]], "Define input": [[58, "Define-input"]], "Compile network": [[58, "Compile-network"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[59, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[59, "Table-of-Contents"]], "Defining the model": [[59, "Defining-the-model"]], "Model construction": [[59, "Model-construction"]], "Some helper functions": [[59, "Some-helper-functions"]], "Transpiling the model": [[59, "Transpiling-the-model"]], "PyTorch pipeline": [[59, "PyTorch-pipeline"]], "Dataset download": [[59, "Dataset-download"]], "DataLoader": [[59, "DataLoader"]], "Training": [[59, "Training"]], "Resnet 18": [[64, "Resnet-18"]], "HuggingFace Tensorflow DeiT": [[62, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[62, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "3.1: Stable Diffusion": [[56, "3.1:-Stable-Diffusion"]], "0.2: Transpile": [[49, "0.2:-Transpile"]], "Ivy as a Transpiler Introduction": [[63, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[63, "To-use-the-transpiler:"]], "Transpiler Interface": [[63, "Transpiler-Interface"]], "Telemetry": [[63, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[63, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[63, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[63, "3.-Transpile-Models-\ud83c\udf10"]], "1.1: Framework Selection": [[51, "1.1:-Framework-Selection"]], "Deepmind PerceiverIO on GPU": [[60, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[60, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[60, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[60, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[60, "Run-the-demo..."]], "\u2026with torch backend": [[60, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[60, "....with-tensorflow-backend"]], "\u2026with jax backend": [[60, "...with-jax-backend"]], "\u2026with numpy backend": [[60, "...with-numpy-backend"]], "lexsort": [[531, "lexsort"]], "instance_norm": [[519, "instance-norm"]], "container_types": [[558, "container-types"]], "cummax": [[539, "cummax"]], "beta": [[525, "beta"]], "current_backend_str": [[559, "current-backend-str"]], "cache_fn": [[555, "cache-fn"]], "optional_get_element": [[549, "optional-get-element"]], "native_sparse_array": [[534, "native-sparse-array"]], "bernoulli": [[524, "bernoulli"]], "nanprod": [[547, "nanprod"]], "assert_supports_inplace": [[554, "assert-supports-inplace"]], "poisson": [[528, "poisson"]], "corrcoef": [[537, "corrcoef"]], "igamma": [[542, "igamma"]], "quantile": [[548, "quantile"]], "cov": [[538, "cov"]], "is_ivy_sparse_array": [[532, "is-ivy-sparse-array"]], "histogram": [[541, "histogram"]], "arg_info": [[551, "arg-info"]], "einops_repeat": [[563, "einops-repeat"]], "clip_matrix_norm": [[556, "clip-matrix-norm"]], "all_equal": [[550, "all-equal"]], "invert_permutation": [[530, "invert-permutation"]], "array_equal": [[553, "array-equal"]], "gamma": [[527, "gamma"]], "nanmin": [[546, "nanmin"]], "local_response_norm": [[522, "local-response-norm"]], "bincount": [[536, "bincount"]], "cummin": [[540, "cummin"]], "group_norm": [[518, "group-norm"]], "l1_normalize": [[520, "l1-normalize"]], "is_native_sparse_array": [[533, "is-native-sparse-array"]], "einops_reduce": [[562, "einops-reduce"]], "median": [[543, "median"]], "einops_rearrange": [[561, "einops-rearrange"]], "nanmean": [[544, "nanmean"]], "dirichlet": [[526, "dirichlet"]], "unravel_index": [[529, "unravel-index"]], "arg_names": [[552, "arg-names"]], "l2_normalize": [[521, "l2-normalize"]], "lp_normalize": [[523, "lp-normalize"]], "default": [[560, "default"]], "native_sparse_array_to_indices_values_and_shape": [[535, "native-sparse-array-to-indices-values-and-shape"]], "nanmedian": [[545, "nanmedian"]], "batch_norm": [[517, "batch-norm"]], "clip_vector_norm": [[557, "clip-vector-norm"]], "unset_nestable_mode": [[623, "unset-nestable-mode"]], "unset_precise_mode": [[624, "unset-precise-mode"]], "unset_queue_timeout": [[625, "unset-queue-timeout"]], "Constants": [[644, "module-ivy.functional.ivy.constants"], [383, "module-ivy.functional.ivy.experimental.constants"]], "to_native_shape": [[614, "to-native-shape"]], "try_else_none": [[617, "try-else-none"]], "jac": [[636, "jac"]], "lamb_update": [[637, "lamb-update"]], "to_numpy": [[615, "to-numpy"]], "Meta": [[657, "meta"], [394, "module-ivy.functional.ivy.experimental.meta"]], "unset_shape_array_mode": [[626, "unset-shape-array-mode"]], "unset_exception_trace_mode": [[619, "unset-exception-trace-mode"]], "gradient_descent_update": [[635, "gradient-descent-update"]], "adam_update": [[632, "adam-update"]], "stop_gradient": [[641, "stop-gradient"]], "unset_tmp_dir": [[628, "unset-tmp-dir"]], "unset_show_func_wrapper_trace_mode": [[627, "unset-show-func-wrapper-trace-mode"]], "unset_inplace_mode": [[620, "unset-inplace-mode"]], "value_is_nan": [[629, "value-is-nan"]], "optimizer_update": [[639, "optimizer-update"]], "value_and_grad": [[642, "value-and-grad"]], "to_ivy_shape": [[612, "to-ivy-shape"]], "to_scalar": [[616, "to-scalar"]], "execute_with_gradients": [[633, "execute-with-gradients"]], "requires_gradient": [[640, "requires-gradient"]], "to_list": [[613, "to-list"]], "unset_min_denominator": [[622, "unset-min-denominator"]], "unset_array_mode": [[618, "unset-array-mode"]], "adam_step": [[631, "adam-step"]], "vmap": [[630, "vmap"]], "grad": [[634, "grad"]], "unset_min_base": [[621, "unset-min-base"]], "lars_update": [[638, "lars-update"]], "Control flow ops": [[645, "control-flow-ops"]], "supports_inplace_updates": [[611, "supports-inplace-updates"]], "partial_unfold": [[503, "partial-unfold"]], "unflatten": [[512, "unflatten"]], "dstack": [[486, "dstack"]], "dsplit": [[485, "dsplit"]], "trim_zeros": [[511, "trim-zeros"]], "column_stack": [[483, "column-stack"]], "pad": [[499, "pad"]], "take_along_axis": [[509, "take-along-axis"]], "check_scalar": [[481, "check-scalar"]], "soft_thresholding": [[507, "soft-thresholding"]], "atleast_2d": [[478, "atleast-2d"]], "pad_sequence": [[500, "pad-sequence"]], "hstack": [[495, "hstack"]], "log_poisson_loss": [[471, "log-poisson-loss"]], "unique_consecutive": [[514, "unique-consecutive"]], "fliplr": [[490, "fliplr"]], "hsplit": [[494, "hsplit"]], "choose": [[482, "choose"]], "expand": [[487, "expand"]], "fill_diagonal": [[488, "fill-diagonal"]], "partial_fold": [[501, "partial-fold"]], "take": [[508, "take"]], "flipud": [[491, "flipud"]], "atleast_3d": [[479, "atleast-3d"]], "vstack": [[516, "vstack"]], "poisson_nll_loss": [[472, "poisson-nll-loss"]], "soft_margin_loss": [[474, "soft-margin-loss"]], "smooth_l1_loss": [[473, "smooth-l1-loss"]], "flatten": [[489, "flatten"]], "fold": [[492, "fold"]], "put_along_axis": [[505, "put-along-axis"]], "associative_scan": [[476, "associative-scan"]], "l1_loss": [[470, "l1-loss"]], "partial_tensor_to_vec": [[502, "partial-tensor-to-vec"]], "concat_from_sequence": [[484, "concat-from-sequence"]], "moveaxis": [[498, "moveaxis"]], "i0": [[496, "i0"]], "partial_vec_to_tensor": [[504, "partial-vec-to-tensor"]], "top_k": [[510, "top-k"]], "matricize": [[497, "matricize"]], "atleast_1d": [[477, "atleast-1d"]], "broadcast_shapes": [[480, "broadcast-shapes"]], "rot90": [[506, "rot90"]], "vsplit": [[515, "vsplit"]], "heaviside": [[493, "heaviside"]], "unfold": [[513, "unfold"]], "as_strided": [[475, "as-strided"]], "truncated_svd": [[464, "truncated-svd"]], "rfft": [[434, "rfft"]], "partial_tucker": [[460, "partial-tucker"]], "hinge_embedding_loss": [[467, "hinge-embedding-loss"]], "multi_mode_dot": [[459, "multi-mode-dot"]], "tucker": [[466, "tucker"]], "higher_order_moment": [[448, "higher-order-moment"]], "adjoint": [[439, "adjoint"]], "mode_dot": [[457, "mode-dot"]], "cond": [[441, "cond"]], "huber_loss": [[468, "huber-loss"]], "pool": [[432, "pool"]], "ifft": [[423, "ifft"]], "eigh_tridiagonal": [[445, "eigh-tridiagonal"]], "dot": [[443, "dot"]], "nearest_interpolate": [[431, "nearest-interpolate"]], "initialize_tucker": [[449, "initialize-tucker"]], "interp": [[425, "interp"]], "kronecker": [[452, "kronecker"]], "diagflat": [[442, "diagflat"]], "general_inner_product": [[447, "general-inner-product"]], "reduce_window": [[433, "reduce-window"]], "kron": [[451, "kron"]], "rnn": [[436, "rnn"]], "sliding_window": [[437, "sliding-window"]], "khatri_rao": [[450, "khatri-rao"]], "interpolate": [[426, "interpolate"]], "lu_factor": [[453, "lu-factor"]], "solve_triangular": [[461, "solve-triangular"]], "max_pool3d": [[429, "max-pool3d"]], "kl_div": [[469, "kl-div"]], "tt_matrix_to_tensor": [[465, "tt-matrix-to-tensor"]], "matrix_exp": [[456, "matrix-exp"]], "max_pool1d": [[427, "max-pool1d"]], "make_svd_non_negative": [[455, "make-svd-non-negative"]], "batched_outer": [[440, "batched-outer"]], "ifftn": [[424, "ifftn"]], "multi_dot": [[458, "multi-dot"]], "rfftn": [[435, "rfftn"]], "max_pool2d": [[428, "max-pool2d"]], "stft": [[438, "stft"]], "svd_flip": [[462, "svd-flip"]], "tensor_train": [[463, "tensor-train"]], "eigvals": [[446, "eigvals"]], "max_unpool1d": [[430, "max-unpool1d"]], "lu_solve": [[454, "lu-solve"]], "gradient": [[364, "gradient"]], "modf": [[370, "modf"]], "indices": [[331, "indices"]], "binarizer": [[352, "binarizer"]], "random_tr": [[340, "random-tr"]], "random_tucker": [[342, "random-tucker"]], "fmax": [[362, "fmax"]], "unsorted_segment_min": [[346, "unsorted-segment-min"]], "random_parafac2": [[339, "random-parafac2"]], "ldexp": [[367, "ldexp"]], "polyval": [[337, "polyval"]], "vorbis_window": [[348, "vorbis-window"]], "amax": [[350, "amax"]], "erfinv": [[359, "erfinv"]], "lgamma": [[369, "lgamma"]], "kaiser_bessel_derived_window": [[332, "kaiser-bessel-derived-window"]], "copysign": [[354, "copysign"]], "amin": [[351, "amin"]], "diff": [[356, "diff"]], "random_cp": [[338, "random-cp"]], "nextafter": [[372, "nextafter"]], "nansum": [[371, "nansum"]], "ndenumerate": [[335, "ndenumerate"]], "ndindex": [[336, "ndindex"]], "count_nonzero": [[355, "count-nonzero"]], "random_tt": [[341, "random-tt"]], "signbit": [[373, "signbit"]], "erfc": [[358, "erfc"]], "hann_window": [[330, "hann-window"]], "lerp": [[368, "lerp"]], "sparsify_tensor": [[375, "sparsify-tensor"]], "allclose": [[349, "allclose"]], "conj": [[353, "conj"]], "tril_indices": [[343, "tril-indices"]], "trilu": [[344, "trilu"]], "fix": [[360, "fix"]], "mel_weight_matrix": [[334, "mel-weight-matrix"]], "float_power": [[361, "float-power"]], "frexp": [[363, "frexp"]], "unsorted_segment_mean": [[345, "unsorted-segment-mean"]], "hypot": [[365, "hypot"]], "kaiser_window": [[333, "kaiser-window"]], "isclose": [[366, "isclose"]], "hamming_window": [[329, "hamming-window"]], "sinc": [[374, "sinc"]], "digamma": [[357, "digamma"]], "unsorted_segment_sum": [[347, "unsorted-segment-sum"]], "Ivy AlexNet demo": [[5, "Ivy-AlexNet-demo"], [4, "Ivy-AlexNet-demo"]], "Installation": [[5, "Installation"], [23, "Installation"], [24, "Installation"], [22, "Installation"], [4, "Installation"]], "Data Preparation": [[5, "Data-Preparation"], [6, "Data-Preparation"], [13, "Data-Preparation"], [23, "Data-Preparation"], [12, "Data-Preparation"], [7, "Data-Preparation"], [22, "Data-Preparation"], [4, "Data-Preparation"]], "Ivy AlexNet inference in Torch": [[5, "Ivy-AlexNet-inference-in-Torch"], [4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[5, "TensorFlow-inference"], [4, "TensorFlow-inference"]], "JAX inference": [[5, "JAX-inference"], [4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[5, "Appendix-(Ivy-code-for-AlexNet-implementation)"], [4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "How To Convert Models from PyTorch to PaddlePaddle": [[10, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"], [11, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[10, "About-the-Model"], [11, "About-the-Model"]], "Transpiling the Model": [[10, "Transpiling-the-Model"], [11, "Transpiling-the-Model"]], "Comparing the results": [[10, "Comparing-the-results"], [9, "Comparing-the-results"], [8, "Comparing-the-results"], [24, "Comparing-the-results"], [11, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[10, "Fine-tuning-the-transpiled-model"], [9, "Fine-tuning-the-transpiled-model"], [8, "Fine-tuning-the-transpiled-model"], [24, "Fine-tuning-the-transpiled-model"], [11, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[10, "Conclusion"], [9, "Conclusion"], [8, "Conclusion"], [24, "Conclusion"], [11, "Conclusion"]], "Using TensorFlow Models in your PyTorch Projects": [[9, "Using-TensorFlow-Models-in-your-PyTorch-Projects"], [8, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[9, "Framework-Incompatibility"], [8, "Framework-Incompatibility"], [24, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[9, "Transpiling-a-TensorFlow-model-to-PyTorch"], [8, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[9, "About-the-transpiled-model"], [8, "About-the-transpiled-model"], [24, "About-the-transpiled-model"]], "Setting-up the source model": [[9, "Setting-up-the-source-model"], [8, "Setting-up-the-source-model"], [24, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[9, "Converting-the-model-from-TensorFlow-to-PyTorch"], [8, "Converting-the-model-from-TensorFlow-to-PyTorch"], [24, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "# Ivy Bert Demo": [[6, "#-Ivy-Bert-Demo"], [7, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[6, "Install-the-dependecies"], [7, "Install-the-dependecies"]], "Import the modules": [[6, "Import-the-modules"], [7, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[6, "Ivy-inference-with-Sequence-Classification"], [7, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[6, "Ivy-model-inference-with-tensorflow"], [7, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[6, "Ivy-model-inference-with-Jax"], [7, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[6, "Ivy-model-inference-with-torch"], [7, "Ivy-model-inference-with-torch"]], "Image Segmentation with Ivy UNet": [[13, "Image-Segmentation-with-Ivy-UNet"], [12, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[13, "Imports"], [27, "Imports"], [23, "Imports"], [12, "Imports"], [22, "Imports"]], "Custom Preprocessing": [[13, "Custom-Preprocessing"], [12, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[13, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [23, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [22, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[13, "Visualise-image"], [23, "Visualise-image"], [12, "Visualise-image"], [22, "Visualise-image"]], "Model Inference": [[13, "Model-Inference"], [12, "Model-Inference"]], "Initializing Native Torch UNet": [[13, "Initializing-Native-Torch-UNet"], [12, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[13, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"], [12, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[13, "Custom-masking-function"], [12, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[13, "Use-the-model-to-segment-your-images-\ud83d\ude80"], [12, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[13, "TensorFlow-backend"], [12, "TensorFlow-backend"]], "JAX": [[13, "JAX"], [12, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[13, "Appendix:-the-Ivy-native-implementation-of-UNet"], [12, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Tutorials And Examples": [[32, "tutorials-and-examples"]], "Learn the basics": [[32, "learn-the-basics"], [33, "learn-the-basics"]], "Examples and Demos": [[32, "examples-and-demos"], [3, "examples-and-demos"]], "Transpile any model": [[41, "Transpile-any-model"]], "Round up": [[41, "Round-up"]], "Transpile code": [[37, "Transpile-code"]], "Accelerating MMPreTrain models with JAX": [[21, "Accelerating-MMPreTrain-models-with-JAX"], [20, "Accelerating-MMPreTrain-models-with-JAX"]], "Transpile any library": [[40, "Transpile-any-library"]], "Accelerating XGBoost with JAX": [[27, "Accelerating-XGBoost-with-JAX"]], "Tests": [[27, "Tests"]], "Loading the Data": [[27, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[27, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[27, "JAX-backend"]], "Tensorflow backend": [[27, "Tensorflow-backend"]], "PyTorch backend": [[27, "PyTorch-backend"]], "More exhaustive example": [[27, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[27, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[27, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[27, "Comparison-of-Metrics"]], "Using Ivy ResNet": [[23, "Using-Ivy-ResNet"], [22, "Using-Ivy-ResNet"]], "Prepare the set of labels": [[23, "Prepare-the-set-of-labels"], [22, "Prepare-the-set-of-labels"]], "Model Inference ResNet34": [[23, "Model-Inference-ResNet34"], [22, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[23, "Initializing-Native-Torch-ResNet34"], [22, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[23, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"], [22, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[23, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [23, "id1"], [22, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [22, "id1"]], "Model Inference ResNet50": [[23, "Model-Inference-ResNet50"], [22, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[23, "Initializing-Native-Torch-ResNet50"], [22, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[23, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"], [22, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "Unify code": [[35, "Unify-code"]], "Quickstart": [[46, "Quickstart"]], "Get familiar with Ivy": [[46, "Get-familiar-with-Ivy"]], "Functional API": [[46, "Functional-API"]], "Stateful API": [[46, "Stateful-API"]], "Tracing code": [[46, "Tracing-code"]], "Any function": [[46, "Any-function"], [45, "Any-function"]], "Any library": [[46, "Any-library"], [45, "Any-library"]], "Any model": [[46, "Any-model"], [45, "Any-model"]], "Training PyTorch ResNet in your TensorFlow Projects": [[24, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Transpiling a PyTorch model to TensorFlow": [[24, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "Load the Data": [[24, "Load-the-Data"]], "Visualize a few images": [[24, "Visualize-a-few-images"]], "Load the pre-trained model": [[24, "Load-the-pre-trained-model"]], "Transpiling a PyTorch model to build on top": [[28, "Transpiling-a-PyTorch-model-to-build-on-top"]], "Write a model using Ivy": [[42, "Write-a-model-using-Ivy"]], "How to use decorators": [[39, "How-to-use-decorators"]], "Trace": [[39, "Trace"], [38, "Trace"]], "Graph Transpile": [[39, "Graph-Transpile"]], "Transpile \ud83d\udea7": [[39, "Transpile-\ud83d\udea7"]], "Transpiling Functions from PyTorch to TensorFlow": [[43, "Transpiling-Functions-from-PyTorch-to-TensorFlow"]], "Transpiling a Tensorflow model to build on top": [[30, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Accelerating PyTorch models with JAX": [[26, "Accelerating-PyTorch-models-with-JAX"], [25, "Accelerating-PyTorch-models-with-JAX"]], "ODSC Ivy Demo": [[45, "ODSC-Ivy-Demo"]], "Ivy as a Framework": [[45, "Ivy-as-a-Framework"]], "Ivy Backend Handler": [[45, "Ivy-Backend-Handler"], [34, "Ivy-Backend-Handler"]], "Data Structures": [[45, "Data-Structures"], [34, "Data-Structures"]], "Ivy Functional API": [[45, "Ivy-Functional-API"], [34, "Ivy-Functional-API"]], "Ivy Stateful API": [[45, "Ivy-Stateful-API"], [34, "Ivy-Stateful-API"]], "Graph Tracer": [[45, "Graph-Tracer"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Transpiling a haiku model to build on top": [[29, "Transpiling-a-haiku-model-to-build-on-top"]], "Write Ivy code": [[34, "Write-Ivy-code"]], "Contents": [[34, "Contents"]], "Installing Ivy": [[34, "Installing-Ivy"]], "Importing Ivy": [[34, "Importing-Ivy"], [0, "Importing-Ivy"]], "Transpiling Models from PyTorch to TensorFlow": [[44, "Transpiling-Models-from-PyTorch-to-TensorFlow"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Trace code": [[36, "Trace-code"]], "Lazy vs Eager": [[38, "Lazy-vs-Eager"]], "Developing a convolutional network using Ivy": [[31, "Developing-a-convolutional-network-using-Ivy"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "sqrt": [[302, "sqrt"]], "blackman_window": [[327, "blackman-window"]], "logit": [[315, "logit"]], "square": [[303, "square"]], "rad2deg": [[294, "rad2deg"]], "subtract": [[304, "subtract"]], "hardtanh": [[314, "hardtanh"]], "prelu": [[317, "prelu"]], "logsigmoid": [[316, "logsigmoid"]], "hardsilu": [[313, "hardsilu"]], "positive": [[292, "positive"]], "logical_xor": [[285, "logical-xor"]], "remainder": [[297, "remainder"]], "hardshrink": [[312, "hardshrink"]], "threshold": [[325, "threshold"]], "logical_and": [[282, "logical-and"]], "maximum": [[286, "maximum"]], "multiply": [[288, "multiply"]], "trunc_divide": [[309, "trunc-divide"]], "real": [[295, "real"]], "negative": [[290, "negative"]], "eye_like": [[328, "eye-like"]], "not_equal": [[291, "not-equal"]], "logical_not": [[283, "logical-not"]], "round": [[298, "round"]], "elu": [[311, "elu"]], "thresholded_relu": [[326, "thresholded-relu"]], "pow": [[293, "pow"]], "minimum": [[287, "minimum"]], "softshrink": [[322, "softshrink"]], "trapz": [[307, "trapz"]], "celu": [[310, "celu"]], "nan_to_num": [[289, "nan-to-num"]], "sinh": [[301, "sinh"]], "tanhshrink": [[324, "tanhshrink"]], "tanh": [[306, "tanh"]], "sin": [[300, "sin"]], "reciprocal": [[296, "reciprocal"]], "stanh": [[323, "stanh"]], "selu": [[320, "selu"]], "tan": [[305, "tan"]], "relu6": [[318, "relu6"]], "scaled_tanh": [[319, "scaled-tanh"]], "sign": [[299, "sign"]], "silu": [[321, "silu"]], "logical_or": [[284, "logical-or"]], "trunc": [[308, "trunc"]], "print_all_ivy_arrays_on_dev": [[223, "print-all-ivy-arrays-on-dev"]], 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"overview/deep_dive/ivy_tests.rst", "overview/deep_dive/navigating_the_code.rst", "overview/deep_dive/operating_modes.rst", "overview/deep_dive/superset_behaviour.rst", "overview/design.rst", "overview/design/building_blocks.rst", "overview/design/ivy_as_a_transpiler.rst", "overview/faq.rst", "overview/get_started.rst", "overview/glossary.rst", "overview/motivation.rst", "overview/motivation/ml_explosion.rst", "overview/motivation/why_transpile.rst", "overview/one_liners.rst", "overview/one_liners/graph_transpile.rst", "overview/one_liners/trace.rst", "overview/one_liners/transpile.rst", "overview/one_liners/unify.rst", "overview/related_work.rst", "overview/related_work/frameworks.rst", "overview/related_work/graph_tracers.rst", "overview/related_work/ivy_vs_onnx.rst"], "titles": ["Credit Card Fraud Detection using Ivy Framework", "Demos", "TO REPLACE: Title", "Examples and Demos", "Ivy AlexNet demo", "Ivy AlexNet demo", "# Ivy Bert Demo", "# Ivy Bert Demo", "Using TensorFlow Models 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use decorators", "Transpile any library", "Transpile any model", "Write a model using Ivy", "Transpiling Functions from PyTorch to TensorFlow", "Transpiling Models from PyTorch to TensorFlow", "ODSC Ivy Demo", "Quickstart", "0.0: Unify", "0.1: Compile", "0.2: Transpile", "1.0: Lazy vs Eager", "1.1: Framework Selection", "1.2: As a Decorator", "1.3: Dynamic vs Static", "2.0: Kornia", "3.0: Perceiver", "3.1: Stable Diffusion", "Basic Operations with Ivy", "Compilation of a Basic Function", "Demo: Transpiling DeepMind\u2019s PerceiverIO", "Deepmind PerceiverIO on GPU", "End-to-End Training Pipeline in Ivy", "HuggingFace Tensorflow DeiT", "Ivy as a Transpiler Introduction", "Resnet 18", "Activations", "Conversions", "Creation", "Data type", "Device", "Elementwise", "Experimental", "General", "Gradients", "Image", "Layers", "Linear algebra", "Losses", "Manipulation", "Norms", "Random", "Searching", "Set", "Sorting", "Statistical", "Utility", "Wrapping", "Activations", "Base", "Conversions", "Creation", "Data type", "Device", "Elementwise", "Experimental", "General", "Gradients", "Image", "Layers", "Linear algebra", "Losses", "Manipulation", "Norms", "Random", "Searching", "Set", "Sorting", "Statistical", "Utility", "Wrapping", "Base", "Cp tensor", "Parafac2 tensor", "Tr tensor", "Tt tensor", "Tucker tensor", "Array", "Container", "Factorized tensor", "Nested array", "Base", "Elementwise", "Data classes", "Functions", "gelu", "hardswish", "leaky_relu", "log_softmax", "mish", "relu", "sigmoid", "softmax", "softplus", "softsign", "cmp_is", "cmp_isnot", "for_loop", "if_else", "try_except", "while_loop", "arange", "array", "asarray", "complex", "copy_array", "empty", "empty_like", "eye", "from_dlpack", "frombuffer", "full", "full_like", "linspace", "logspace", "meshgrid", "native_array", "one_hot", "ones", "ones_like", "to_dlpack", "tril", "triu", "triu_indices", "zeros", "zeros_like", "as_ivy_dtype", "as_native_dtype", "astype", "broadcast_arrays", "broadcast_to", "can_cast", 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"batch_norm", "group_norm", "instance_norm", "l1_normalize", "l2_normalize", "local_response_norm", "lp_normalize", "bernoulli", "beta", "dirichlet", "gamma", "poisson", "unravel_index", "invert_permutation", "lexsort", "is_ivy_sparse_array", "is_native_sparse_array", "native_sparse_array", "native_sparse_array_to_indices_values_and_shape", "bincount", "corrcoef", "cov", "cummax", "cummin", "histogram", "igamma", "median", "nanmean", "nanmedian", "nanmin", "nanprod", "quantile", "optional_get_element", "all_equal", "arg_info", "arg_names", "array_equal", "assert_supports_inplace", "cache_fn", "clip_matrix_norm", "clip_vector_norm", "container_types", "current_backend_str", "default", "einops_rearrange", "einops_reduce", "einops_repeat", "exists", "fourier_encode", "function_supported_devices_and_dtypes", "function_unsupported_devices_and_dtypes", "gather", "gather_nd", "get_all_arrays_in_memory", "get_item", "get_num_dims", "get_referrers_recursive", "has_nans", "inplace_arrays_supported", "inplace_decrement", "inplace_increment", "inplace_update", "inplace_variables_supported", "is_array", "is_ivy_array", "is_ivy_container", "is_ivy_nested_array", "is_native_array", "isin", "isscalar", "itemsize", "match_kwargs", "multiprocessing", "num_arrays_in_memory", "print_all_arrays_in_memory", "scatter_flat", "scatter_nd", "set_array_mode", "set_exception_trace_mode", "set_inplace_mode", "set_item", "set_min_base", "set_min_denominator", "set_nestable_mode", "set_precise_mode", "set_queue_timeout", "set_shape_array_mode", "set_show_func_wrapper_trace_mode", "set_tmp_dir", "shape", "size", "stable_divide", "stable_pow", "strides", "supports_inplace_updates", "to_ivy_shape", "to_list", "to_native_shape", "to_numpy", "to_scalar", "try_else_none", "unset_array_mode", "unset_exception_trace_mode", "unset_inplace_mode", "unset_min_base", "unset_min_denominator", "unset_nestable_mode", "unset_precise_mode", "unset_queue_timeout", "unset_shape_array_mode", "unset_show_func_wrapper_trace_mode", "unset_tmp_dir", "value_is_nan", "vmap", "adam_step", "adam_update", "execute_with_gradients", "grad", "gradient_descent_update", "jac", "lamb_update", "lars_update", "optimizer_update", "requires_gradient", "stop_gradient", "value_and_grad", "Activations", "Constants", "Control flow ops", "Creation", "Data type", "Device", "Elementwise", "Experimental", "General", "Gradients", "Layers", "Linear algebra", "Losses", "Manipulation", "Meta", "Nest", "Norms", "Random", "Searching", "Set", "Sorting", "Statistical", "Utility", "conv", "conv1d", "conv1d_transpose", "conv2d", "conv2d_transpose", "conv3d", "conv3d_transpose", "conv_general_dilated", "conv_general_transpose", "depthwise_conv2d", "dropout", "linear", "lstm", "lstm_update", "multi_head_attention", "nms", "roi_align", "scaled_dot_product_attention", "cholesky", "cross", "det", "diag", "diagonal", "eig", "eigh", "eigvalsh", "inner", "inv", "matmul", "matrix_norm", "matrix_power", "matrix_rank", 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"Contributor Rewards", "Error Handling", "Helpful Resources", "Open Tasks", "Setting Up", "The Basics", "Contributor Program", "Deep Dive", "Array API Tests", "Arrays", "Backend Setting", "Building the Docs Pipeline", "Containers", "Continuous Integration", "Data Types", "Devices", "Docstring Examples", "Docstrings", "Exception Handling", "Fix Failing Tests:", "Formatting", "Function Arguments", "Function Types", "Function Wrapping", "Gradients", "Inplace Updates", "Ivy Frontends", "Ivy Frontend Tests", "Ivy-Lint: Ivy\u2019s Custom Code Formatters", "Ivy Tests", "Navigating the Code", "Operating Modes", "Superset Behaviour", "Design", "Building Blocks", "Ivy as a Transpiler", "FAQ", "Get Started", "Glossary", "Motivation", "ML Explosion", "Why Transpile?", "One liners", "<code class=\"docutils literal notranslate\"><span class=\"pre\">ivy.graph_transpile()</span></code>", "<code class=\"docutils literal notranslate\"><span class=\"pre\">ivy.trace_graph()</span></code>", "<code 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434, 450, 452, 459, 467, 468, 469, 470, 471, 472, 473, 474, 477, 478, 479, 483, 484, 501, 504, 506, 508, 509, 510, 512, 517, 519, 520, 521, 523, 525, 538, 539, 540, 541, 550, 553, 554, 556, 557, 561, 562, 563, 564, 565, 568, 569, 572, 574, 576, 577, 578, 580, 581, 584, 592, 593, 607, 608, 609, 611, 613, 615, 616, 629, 635, 641, 658, 667, 668, 669, 670, 676, 677, 683, 684, 685, 690, 691, 692, 693, 694, 695, 697, 699, 701, 702, 708, 713, 714, 715, 719, 723, 726, 727, 728, 729, 730, 733, 734, 751, 758, 759, 760, 761, 763, 766, 769, 770, 771, 772, 773, 777, 778, 781, 783, 784, 786, 787, 788, 797, 827, 847, 858, 865], "colab": [2, 6, 7, 24, 25, 26, 27, 28, 30, 34, 35, 36, 38, 39, 40, 41, 46, 59, 61, 63, 64], "manual": [2, 8, 9, 10, 11, 24, 25, 26, 27, 28, 30, 34, 35, 36, 38, 39, 40, 41, 46, 658, 738, 748, 749, 839, 840, 841, 850, 856, 865, 871], "mind": [2, 28, 30, 34, 40, 45, 49, 839, 840, 845, 848, 865, 873, 879], "click": [2, 4, 5, 61, 839, 840, 841, 849, 853, 855, 856], "runtim": [2, 4, 5, 6, 7, 12, 13, 20, 21, 22, 23, 24, 25, 26, 36, 45, 48, 59, 60, 843, 858, 865, 868, 884], "restart": [2, 4, 5, 6, 7, 12, 13, 22, 23, 24, 59, 60, 840, 855], "git": [2, 4, 5, 6, 7, 12, 13, 22, 23, 45, 59, 60, 61, 62, 833, 835, 838, 840, 841, 844, 847, 849, 855, 856, 865, 873], "clone": [2, 4, 5, 12, 13, 22, 23, 45, 59, 61, 62, 833, 835, 841, 855, 873], "http": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 24, 25, 26, 30, 38, 40, 41, 45, 46, 59, 60, 61, 62, 63, 64, 70, 71, 93, 94, 96, 162, 170, 258, 268, 269, 284, 343, 350, 351, 384, 387, 390, 393, 402, 434, 508, 538, 631, 632, 646, 647, 649, 652, 654, 656, 664, 702, 703, 734, 784, 833, 835, 840, 841, 844, 847, 849, 850, 853, 855, 873, 879], "github": [2, 4, 5, 6, 7, 12, 13, 20, 21, 22, 23, 25, 26, 45, 59, 60, 61, 62, 63, 833, 835, 836, 838, 841, 842, 844, 847, 849, 850, 852, 853, 855, 856, 864, 865, 873], "com": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 25, 26, 30, 45, 59, 60, 61, 62, 63, 833, 835, 840, 841, 844, 847, 849, 850, 855, 873], "llc": [2, 4, 5, 12, 13, 22, 23, 45, 59, 60, 61, 62, 833, 873], "model": [2, 3, 4, 5, 16, 17, 27, 32, 33, 34, 62, 64, 255, 288, 392, 468, 649, 809, 813, 814, 818, 832, 833, 871, 874, 876, 877, 879, 880, 884, 885, 886], "depth": [2, 4, 5, 8, 9, 12, 13, 22, 23, 60, 67, 71, 75, 90, 94, 98, 156, 390, 393, 426, 486, 561, 573, 646, 651, 653, 671, 672, 841, 849, 870, 873], "repositori": [2, 4, 5, 12, 13, 22, 23, 835, 839, 840, 841, 843, 844, 847, 855, 864], "cd": [2, 4, 5, 12, 13, 22, 23, 45, 62, 833, 835, 840, 841, 855, 873], "acceler": [3, 32, 850, 862, 877], "convert": [3, 12, 13, 15, 16, 17, 20, 21, 25, 26, 27, 28, 30, 32, 35, 37, 40, 41, 43, 44, 45, 46, 47, 49, 51, 59, 62, 64, 66, 67, 70, 88, 89, 90, 93, 111, 141, 142, 155, 165, 166, 208, 209, 210, 211, 222, 230, 234, 254, 294, 393, 398, 477, 478, 479, 529, 594, 612, 614, 615, 616, 618, 646, 647, 648, 649, 651, 654, 658, 712, 739, 750, 751, 793, 822, 827, 839, 845, 846, 859, 860, 862, 865, 867, 870, 871, 872, 874, 875, 877, 878, 879, 881, 886], "faster": [3, 4, 5, 16, 17, 20, 21, 25, 26, 27, 32, 45, 46, 62, 64, 71, 76, 94, 99, 391, 464, 654, 704, 835, 838, 847, 874], "infer": [3, 8, 9, 10, 11, 16, 17, 20, 21, 24, 25, 26, 27, 32, 36, 48, 50, 51, 60, 62, 64, 67, 71, 72, 75, 78, 90, 94, 95, 98, 101, 140, 142, 146, 150, 151, 155, 158, 164, 173, 174, 175, 176, 177, 327, 328, 390, 393, 397, 426, 512, 526, 572, 606, 607, 646, 647, 651, 653, 656, 676, 726, 822, 823, 843, 846, 850, 851, 865, 870, 879, 886], "finetun": [3, 32, 59], "project": [3, 22, 23, 25, 26, 32, 37, 38, 40, 41, 45, 46, 49, 112, 653, 680, 812, 833, 835, 836, 839, 840, 841, 842, 845, 846, 847, 865, 872, 877, 879, 884], "resnet": [3, 8, 9, 25, 26, 32, 45, 879, 880], "video": [4, 12, 20, 22, 25, 28, 30, 34, 35, 36, 37, 38, 39, 40, 41, 46, 833, 834, 839, 840, 841, 844, 845, 846, 848, 849, 850, 851, 852, 853, 854, 856, 857, 858, 859, 860, 861, 862, 863, 865, 866, 868, 873], "tutori": [4, 8, 9, 10, 11, 12, 20, 22, 24, 25, 28, 30, 34, 35, 36, 37, 38, 39, 40, 41, 46, 833, 841, 862, 873], "written": [4, 5, 6, 7, 8, 9, 24, 34, 44, 45, 46, 59, 72, 393, 488, 840, 844, 845, 853, 856, 857, 861, 862, 866, 870, 871, 872, 879, 881, 884], "imag": [4, 5, 8, 9, 10, 11, 20, 21, 25, 26, 28, 40, 45, 46, 59, 60, 61, 62, 63, 64, 71, 75, 93, 94, 98, 116, 143, 235, 236, 237, 238, 241, 244, 253, 256, 258, 260, 269, 270, 271, 276, 278, 291, 298, 299, 301, 302, 306, 390, 409, 410, 426, 427, 428, 430, 561, 646, 649, 651, 653, 655, 666, 667, 668, 669, 670, 673, 674, 675, 716, 812, 833, 840, 855, 868, 873, 879, 880, 884], "classif": [4, 5, 22, 23, 27, 59, 884], "three": [4, 5, 6, 7, 32, 38, 50, 51, 61, 71, 154, 327, 384, 393, 479, 646, 840, 841, 848, 849, 850, 852, 862, 865, 868, 871, 885], "major": [4, 5, 6, 7, 661, 767, 850, 851, 863, 865, 872, 884], "ml": [4, 5, 6, 7, 8, 9, 24, 32, 33, 34, 35, 36, 37, 38, 40, 41, 45, 46, 47, 48, 49, 50, 51, 52, 59, 61, 64, 833, 834, 838, 862, 871, 875, 877, 879, 883, 884, 886], "framework": [4, 5, 6, 7, 10, 11, 15, 16, 17, 28, 30, 34, 35, 36, 37, 38, 40, 41, 46, 47, 48, 49, 50, 52, 59, 61, 63, 66, 72, 185, 207, 217, 220, 231, 559, 575, 579, 611, 614, 647, 648, 651, 658, 740, 791, 793, 797, 804, 809, 816, 822, 823, 836, 837, 839, 840, 843, 844, 845, 846, 847, 849, 850, 851, 852, 854, 855, 857, 858, 859, 861, 862, 865, 866, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 885, 886], "sinc": [4, 5, 12, 13, 22, 23, 24, 40, 41, 45, 46, 59, 61, 71, 94, 112, 387, 835, 840, 841, 844, 845, 846, 847, 848, 849, 850, 851, 854, 861, 862, 872, 884], "automat": [4, 5, 12, 13, 16, 17, 22, 23, 24, 41, 45, 46, 51, 839, 840, 841, 843, 846, 847, 849, 850, 856, 858, 861, 865, 868, 874, 876, 884], "sure": [4, 5, 12, 13, 20, 21, 22, 23, 24, 25, 26, 27, 45, 59, 836, 839, 840, 841, 844, 849, 854, 855, 862, 863, 865, 868], "enabl": [4, 5, 6, 7, 8, 9, 12, 13, 20, 21, 22, 23, 24, 25, 26, 27, 38, 41, 60, 71, 76, 88, 99, 117, 390, 392, 413, 471, 596, 640, 651, 652, 654, 697, 814, 832, 833, 840, 841, 842, 845, 848, 850, 858, 859, 860, 861, 862, 865, 866, 870, 871, 872, 874, 877, 880, 884, 885, 886], "dm": [4, 5, 6, 7, 12, 13, 20, 21, 25, 26, 45, 46, 57, 59], "haiku": [4, 5, 6, 7, 12, 13, 20, 21, 25, 26, 41, 45, 46, 57, 59, 63, 809, 833, 879, 884], "exit": [4, 12, 22, 24, 45, 46, 851], "download": [4, 5, 8, 9, 10, 11, 22, 23, 24, 28, 30, 45, 46, 60, 61, 64, 835, 840, 847, 865, 879, 880], "imagenet": [4, 5, 8, 9, 24, 30, 60, 62, 833], "class": [4, 5, 8, 9, 10, 11, 12, 13, 15, 22, 23, 24, 27, 28, 30, 34, 44, 45, 46, 57, 58, 59, 60, 61, 62, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 121, 149, 158, 164, 180, 183, 196, 198, 199, 258, 295, 353, 375, 387, 401, 402, 410, 411, 444, 544, 545, 552, 561, 565, 578, 588, 611, 646, 647, 648, 649, 651, 653, 654, 655, 658, 659, 674, 679, 683, 689, 699, 703, 704, 706, 713, 732, 739, 750, 757, 772, 779, 783, 784, 793, 794, 801, 802, 803, 804, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 821, 822, 825, 827, 832, 839, 846, 847, 848, 850, 851, 852, 853, 857, 859, 860, 863, 864, 865, 868, 870, 871, 874, 878, 879, 880, 881, 884, 885], "wget": [4, 5, 8, 9, 12, 13, 22, 23, 59, 60, 63, 840], "raw": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 25, 26, 40, 45, 46, 59, 62, 63, 88, 833, 853, 879, 885], "githubusercont": [4, 5, 8, 9, 12, 13, 22, 23, 59, 63], "hub": [4, 5, 8, 9, 12, 13, 22, 23, 59, 62, 64], "master": [4, 5, 12, 13, 22, 23, 35, 36, 37, 47, 48, 49, 50, 51, 52, 59, 61, 62, 63, 836, 849, 884], "imagenet_class": [4, 5, 22, 23], "categori": [4, 5, 8, 9, 22, 23, 839, 844, 845, 848, 850, 854, 862, 866], "strip": [4, 5, 22, 23, 36, 48], "readlin": [4, 5, 22, 23, 60], "cat": [4, 5, 10, 11, 22, 23, 60, 863, 868, 870, 871, 879, 880], "jpg": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 25, 26, 40, 45, 46, 61, 62, 833, 879], "filenam": [4, 5, 12, 13, 22, 23, 24, 45, 46, 59, 61, 64, 72, 814, 821], "import": [4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 28, 30, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 59, 60, 62, 63, 64, 71, 82, 86, 90, 94, 109, 209, 210, 214, 226, 322, 402, 538, 573, 589, 648, 651, 655, 657, 662, 716, 736, 737, 772, 804, 818, 822, 823, 833, 838, 839, 840, 841, 842, 844, 845, 846, 847, 848, 850, 851, 852, 853, 856, 859, 860, 861, 862, 863, 864, 865, 866, 870, 871, 872, 873, 879, 880, 882, 883, 884], "devic": [4, 5, 8, 9, 10, 11, 12, 16, 17, 20, 21, 22, 23, 24, 25, 26, 60, 61, 64, 67, 71, 80, 88, 90, 94, 103, 116, 119, 120, 121, 140, 141, 142, 145, 146, 147, 150, 151, 152, 153, 155, 156, 157, 158, 160, 161, 162, 163, 164, 208, 209, 210, 211, 212, 213, 214, 215, 216, 221, 222, 223, 224, 226, 227, 228, 229, 230, 232, 234, 327, 328, 343, 344, 384, 397, 487, 524, 525, 527, 528, 552, 566, 567, 646, 651, 660, 758, 759, 760, 761, 791, 793, 794, 809, 811, 812, 813, 814, 815, 816, 817, 819, 832, 841, 843, 846, 850, 854, 858, 859, 863, 865, 866, 868, 870, 872, 874, 884], "torchvis": [4, 5, 8, 9, 20, 21, 22, 23, 24, 59], "transform": [4, 5, 6, 7, 8, 9, 10, 11, 20, 21, 22, 23, 24, 25, 26, 40, 45, 46, 59, 60, 62, 71, 75, 94, 98, 390, 391, 412, 413, 418, 419, 422, 423, 424, 434, 435, 438, 455, 653, 677, 796, 799, 812, 833, 859, 865, 874, 879, 880, 884, 885], "pil": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 25, 26, 40, 45, 46, 60, 61, 62, 833, 879], "time": [4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 20, 21, 24, 25, 26, 41, 45, 46, 51, 59, 61, 62, 63, 71, 73, 76, 82, 94, 96, 105, 111, 112, 149, 356, 387, 390, 391, 393, 402, 419, 424, 436, 438, 459, 466, 499, 506, 538, 632, 637, 646, 652, 653, 654, 656, 657, 661, 662, 676, 679, 694, 732, 735, 736, 737, 764, 765, 769, 770, 812, 813, 814, 832, 839, 840, 841, 844, 846, 848, 849, 850, 852, 855, 857, 858, 859, 861, 862, 865, 866, 870, 871, 872, 873, 876, 879, 880, 884, 885], "filterwarn": [4, 5, 6, 7, 24], "ignor": [4, 5, 6, 7, 24, 58, 66, 67, 71, 88, 94, 154, 390, 391, 393, 402, 414, 415, 416, 445, 453, 461, 502, 503, 507, 546, 646, 653, 658, 680, 749, 750, 816, 840, 847, 849, 852, 865, 872], "compos": [4, 5, 8, 9, 10, 11, 20, 21, 22, 23, 24, 45, 46, 59, 71, 94, 390, 404, 405, 406, 407, 840, 848, 862, 865, 880, 882, 884], "resiz": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 22, 23, 24, 59, 60, 71, 94, 390, 426, 868], "centercrop": [4, 5, 22, 23, 24], "224": [4, 5, 8, 9, 10, 11, 22, 23, 24, 28, 30, 45, 46, 59, 60, 62, 833, 879], "totensor": [4, 5, 8, 9, 10, 11, 20, 21, 22, 23, 24, 59], "485": [4, 5, 22, 23, 24, 59], "456": [4, 5, 22, 23, 24, 59, 865], "406": [4, 5, 22, 23, 24, 59, 71, 94, 412, 556, 651], "229": [4, 5, 22, 23, 24, 59, 294, 649], "225": [4, 5, 22, 23, 24, 59, 61, 249, 649], "torch_img": [4, 5, 12, 13, 22, 23], "unsqueez": [4, 5, 12, 13, 20, 21, 22, 23], "img": [4, 5, 12, 13, 22, 23, 40, 45, 46, 59, 60, 61, 63, 833, 879], "ipython": [4, 5, 12, 13, 22, 23, 38, 40, 41, 45, 46, 64], "displai": [4, 5, 12, 13, 22, 23, 24, 40, 45, 46, 59, 60, 61, 63, 64, 840, 847, 849, 854, 865], "end": [4, 5, 12, 13, 24, 59, 60, 71, 94, 140, 243, 299, 368, 387, 390, 392, 393, 438, 467, 489, 499, 502, 503, 646, 649, 828, 840, 841, 846, 849, 855, 861, 866, 868, 872, 886], "set_default_devic": [4, 6, 8, 9, 12, 13, 20, 22, 24, 25, 232, 648, 851], "ivy_model": [4, 5, 6, 7, 12, 13, 22, 23, 62], "ivy_alexnet": [4, 5], "quick": [4, 5, 32, 46, 841, 843, 863], "trace_graph": [4, 5, 6, 7, 12, 13, 22, 23, 36, 37, 38, 39, 45, 46, 48, 49, 50, 51, 52, 53, 62, 814, 871, 878], "moment": [4, 5, 71, 73, 94, 96, 391, 448, 631, 632, 637, 652, 816, 832, 839, 846, 872, 879, 880], "cost": [4, 5, 73, 96, 631, 632, 635, 637, 638, 639, 652, 657, 735, 736, 737, 828, 850, 868], "arg": [4, 5, 8, 9, 12, 13, 16, 17, 18, 20, 21, 22, 23, 24, 28, 30, 38, 39, 41, 45, 46, 50, 51, 52, 63, 66, 88, 110, 120, 136, 218, 228, 617, 645, 646, 648, 651, 791, 793, 808, 809, 812, 813, 814, 819, 822, 827, 832, 833, 845, 850, 851, 854, 860, 861, 862, 868, 870, 879, 880, 882], "asarrai": [4, 5, 6, 7, 12, 13, 20, 21, 22, 23, 60, 67, 71, 72, 83, 90, 94, 95, 106, 141, 400, 530, 531, 561, 572, 576, 577, 607, 608, 609, 646, 651, 653, 662, 663, 667, 770, 774, 854, 859, 862, 863], "cuda": [4, 5, 6, 8, 9, 10, 11, 12, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 34, 45, 60, 61, 64, 67, 71, 80, 90, 94, 103, 152, 153, 156, 208, 209, 210, 226, 397, 524, 525, 527, 528, 646, 648, 654, 660, 705, 758, 759, 760, 761, 811, 812, 813, 814, 815, 816, 817, 832, 870, 871, 872, 874], "output": [4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 18, 22, 23, 24, 34, 40, 41, 43, 45, 46, 58, 59, 60, 62, 65, 67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 116, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 140, 141, 142, 143, 144, 145, 146, 147, 148, 150, 151, 152, 153, 154, 156, 157, 158, 159, 160, 161, 163, 164, 167, 169, 194, 228, 229, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 332, 333, 337, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 379, 380, 381, 382, 384, 387, 389, 390, 391, 392, 393, 396, 397, 398, 400, 402, 403, 404, 405, 406, 407, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 423, 424, 426, 427, 428, 429, 432, 434, 435, 436, 438, 439, 441, 442, 443, 445, 447, 450, 451, 453, 456, 457, 458, 459, 461, 462, 465, 467, 468, 469, 470, 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862, 864, 865, 868, 884], "logit": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 22, 23, 24, 59, 60, 61, 62, 71, 77, 94, 100, 382, 397, 524, 527, 655, 713, 715, 808, 880], "gather": [4, 5, 22, 23, 59, 71, 72, 94, 95, 345, 346, 347, 384, 569, 571, 651], "to_list": [4, 5, 22, 23, 72, 95, 651], "arrai": [4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 34, 35, 36, 38, 39, 40, 41, 45, 46, 47, 48, 50, 51, 52, 57, 58, 59, 60, 61, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 111, 112, 114, 117, 120, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 136, 137, 139, 140, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 167, 168, 169, 170, 173, 174, 175, 176, 177, 178, 180, 183, 184, 186, 187, 188, 190, 192, 193, 194, 195, 201, 211, 212, 216, 221, 223, 225, 228, 229, 233, 235, 236, 237, 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"dropout": [4, 5, 75, 98, 390, 414, 415, 416, 653, 678, 680, 683, 812], "torch_output": [4, 5, 12, 13, 15, 16, 17, 22, 23], "dim": [4, 5, 22, 23, 61, 71, 88, 90, 94, 156, 328, 384, 390, 393, 408, 418, 419, 420, 423, 431, 489, 512, 646, 653, 666, 673, 674, 679, 798, 812, 850, 862, 863, 868], "torch_class": [4, 5, 22, 23], "torch_logit": [4, 5, 22, 23], "tensor": [4, 5, 6, 7, 8, 9, 16, 17, 20, 21, 22, 23, 24, 25, 26, 28, 30, 34, 35, 38, 39, 41, 43, 45, 46, 47, 51, 57, 59, 67, 70, 71, 72, 75, 76, 77, 78, 80, 84, 88, 90, 93, 94, 95, 98, 99, 100, 101, 103, 107, 110, 144, 152, 153, 156, 162, 178, 194, 286, 287, 317, 334, 338, 339, 340, 341, 342, 343, 352, 375, 382, 384, 387, 390, 391, 392, 393, 402, 403, 409, 410, 413, 417, 426, 427, 428, 429, 436, 438, 440, 447, 448, 449, 450, 453, 455, 457, 459, 460, 463, 465, 466, 467, 469, 472, 473, 489, 492, 497, 500, 501, 502, 503, 504, 507, 512, 513, 544, 549, 592, 593, 646, 647, 649, 651, 653, 654, 655, 656, 660, 664, 676, 679, 680, 695, 706, 713, 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868, 870, 871, 879, 880], "ll": [4, 5, 8, 9, 10, 11, 12, 13, 16, 17, 20, 21, 24, 25, 26, 34, 35, 36, 37, 38, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 60, 833, 834, 836, 837, 839, 840, 841, 842, 847, 852, 855, 856, 860, 861, 879, 880], "try": [4, 5, 8, 9, 10, 11, 24, 35, 47, 57, 60, 64, 88, 617, 651, 811, 822, 833, 839, 840, 841, 844, 845, 848, 849, 850, 854, 856, 861, 863, 870, 872, 882], "tf": [4, 5, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 24, 25, 26, 28, 30, 35, 38, 39, 41, 43, 44, 45, 46, 47, 48, 50, 52, 57, 62, 63, 809, 818, 833, 845, 850, 851, 857, 861, 862, 865, 866, 868, 870, 871, 872, 874, 879, 880, 882, 884], "onc": [4, 5, 8, 9, 12, 13, 45, 46, 57, 59, 76, 80, 99, 103, 228, 391, 444, 648, 654, 660, 689, 690, 691, 704, 758, 839, 840, 841, 848, 849, 850, 851, 852, 855, 856, 861, 862, 865, 868, 870, 872, 880], "set": [4, 5, 10, 11, 16, 17, 28, 30, 36, 45, 46, 48, 51, 59, 60, 61, 62, 63, 66, 71, 72, 75, 76, 81, 83, 84, 88, 94, 95, 98, 99, 104, 106, 107, 129, 132, 139, 160, 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21, 796, 844, 863, 865], "perf_count": [4, 5, 16, 17, 18, 20, 21], "raw_logit": [4, 5], "latenc": [4, 5, 20, 21], "nn": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 30, 41, 44, 45, 46, 59, 63, 154, 646, 818, 833, 858, 863, 868, 870, 871, 879, 885], "direct": [4, 5, 71, 94, 356, 363, 367, 372, 376, 387, 390, 393, 424, 435, 490, 491, 506, 663, 776, 839, 845, 847, 862, 868, 870, 883, 884, 885], "tolist": [4, 5], "652289830999962": [4, 5], "int32": [4, 5, 57, 59, 68, 71, 72, 80, 81, 84, 91, 94, 95, 103, 104, 147, 152, 156, 158, 164, 167, 170, 172, 174, 176, 178, 181, 183, 184, 188, 191, 195, 199, 203, 205, 223, 250, 286, 287, 398, 402, 529, 539, 540, 541, 569, 578, 615, 646, 647, 648, 649, 651, 660, 661, 664, 759, 760, 761, 765, 777, 778, 783, 785, 796, 797, 850, 862, 865, 870], "6477362": [4, 5], "29496726": [4, 5], "04526032": [4, 5], "As": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 24, 25, 26, 27, 28, 30, 36, 40, 41, 45, 46, 48, 51, 57, 58, 82, 86, 109, 654, 662, 702, 769, 770, 771, 772, 837, 839, 840, 841, 842, 845, 847, 848, 849, 850, 851, 854, 855, 856, 857, 858, 861, 862, 863, 864, 865, 868, 872, 879, 880, 882, 884], "ident": [4, 5, 8, 9, 16, 17, 27, 41, 43, 60, 62, 76, 88, 147, 216, 571, 597, 646, 648, 651, 654, 658, 692, 696, 751, 812, 818, 848, 858, 859, 862, 863, 866, 868, 872, 874], "had": [4, 5, 848, 849, 861, 866, 870, 871, 884, 885], "postprocess": [4, 5], "routin": [4, 5, 849, 861, 862, 868, 872, 884], "feed": [4, 5, 228, 648, 880, 884, 885], "carefulli": [4, 5, 293, 649, 811, 862], "rewrit": [4, 5], "easili": [4, 5, 40, 45, 46, 57, 840, 845, 849, 855, 862, 865, 868, 871, 872, 877, 884], "quickest": [4, 5], "particular": [4, 5, 45, 46, 283, 649, 797, 840, 841, 844, 846, 849, 850, 852, 859, 861, 862, 865, 866, 884], "again": [4, 5, 12, 13, 37, 38, 48, 49, 50, 51, 654, 702, 841, 845, 846, 847, 848, 852, 854, 856, 861, 862, 865, 866, 868, 870, 872], "speed": [4, 5, 20, 21, 25, 26, 27, 45, 46, 59, 64, 72, 95, 585, 651, 865], "repeat": [4, 5, 6, 7, 37, 49, 71, 72, 78, 94, 95, 101, 390, 393, 402, 419, 424, 488, 538, 563, 651, 656, 657, 732, 736, 737, 827, 841, 845, 846, 852, 853, 861, 865], "previou": [4, 5, 27, 36, 37, 38, 40, 48, 49, 50, 52, 73, 94, 96, 202, 203, 204, 205, 206, 379, 389, 390, 436, 618, 620, 621, 622, 623, 625, 626, 628, 632, 637, 647, 651, 652, 811, 831, 840, 841, 844, 846, 849, 851, 857, 862, 865, 868, 872], "trace": [4, 5, 6, 7, 8, 9, 12, 13, 20, 21, 22, 23, 24, 25, 26, 32, 33, 37, 40, 45, 48, 50, 51, 63, 72, 76, 88, 95, 99, 580, 581, 584, 595, 604, 619, 627, 651, 654, 793, 804, 814, 816, 832, 833, 844, 848, 850, 862, 867, 868, 871, 872, 878, 879, 880, 885], "026875037000081647": [4, 5], "overrid": [4, 5, 12, 13, 51, 60, 67, 71, 90, 94, 156, 402, 538, 646, 845, 847], "prealloc": [4, 5, 12, 13], "temporari": [4, 5, 12, 13, 605, 628, 651, 828, 850, 867], "fix": [4, 5, 12, 13, 61, 71, 94, 111, 112, 387, 390, 391, 436, 466, 653, 680, 833, 837, 840, 841, 844, 850, 856, 865, 866], "until": [4, 5, 12, 13, 828, 841, 861, 870, 872, 880], "o": [4, 5, 12, 13, 24, 58, 59, 60, 61, 63, 588, 651, 653, 680, 833, 840, 843, 849, 871, 873], "environ": [4, 5, 12, 13, 25, 26, 38, 40, 41, 60, 63, 833, 834, 841, 873, 876, 884, 886], "xla_python_client_alloc": [4, 5, 12, 13], "platform": [4, 5, 8, 9, 12, 13, 24, 27, 38, 41, 835, 838, 840, 847, 886], "jit": [4, 5, 20, 21, 25, 26, 45, 48, 871, 872, 880, 884], "img_jax": [4, 5, 12, 13], "device_put": [4, 5, 20, 21], "warm": [4, 5], "_": [4, 5, 16, 17, 18, 20, 21, 25, 26, 27, 45, 58, 59, 70, 71, 88, 93, 94, 96, 112, 170, 258, 260, 268, 269, 284, 350, 351, 387, 390, 393, 402, 434, 463, 466, 508, 538, 561, 631, 632, 647, 649, 651, 652, 654, 656, 658, 664, 702, 703, 705, 734, 745, 784, 841, 849, 850, 853, 861, 865], "0022192720000475674": [4, 5], "64773613": [4, 5], "29496723": [4, 5], "exact": [4, 5, 71, 87, 88, 124, 390, 392, 426, 431, 471, 472, 662, 769, 771, 798, 808, 840, 841, 844, 852, 871], "note": [4, 5, 8, 9, 12, 13, 24, 27, 45, 46, 51, 60, 61, 62, 71, 72, 76, 78, 82, 94, 99, 101, 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814, 815, 816, 817, 818, 821, 822, 825, 827, 829, 832, 833, 839, 845, 846, 850, 854, 862, 866, 871, 879], "self": [4, 5, 8, 9, 10, 11, 12, 13, 28, 30, 44, 45, 46, 57, 58, 59, 61, 63, 65, 67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 116, 117, 120, 124, 125, 126, 127, 128, 129, 130, 131, 132, 142, 144, 146, 148, 149, 151, 152, 153, 154, 155, 156, 158, 160, 161, 162, 164, 167, 168, 169, 170, 178, 180, 183, 186, 187, 188, 190, 192, 195, 212, 229, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 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"einops_rearrange": [[561, "einops-rearrange"]], "median": [[543, "median"]], "array_equal": [[553, "array-equal"]], "lp_normalize": [[523, "lp-normalize"]], "lexsort": [[531, "lexsort"]], "nanmean": [[544, "nanmean"]], "is_native_sparse_array": [[533, "is-native-sparse-array"]], "poisson": [[528, "poisson"]], "nanmin": [[546, "nanmin"]], "arg_info": [[551, "arg-info"]], "group_norm": [[518, "group-norm"]], "einops_reduce": [[562, "einops-reduce"]], "cache_fn": [[555, "cache-fn"]], "container_types": [[558, "container-types"]], "native_sparse_array": [[534, "native-sparse-array"]], "all_equal": [[550, "all-equal"]], "gamma": [[527, "gamma"]], "igamma": [[542, "igamma"]], "instance_norm": [[519, "instance-norm"]], "nanprod": [[547, "nanprod"]], "arg_names": [[552, "arg-names"]], "unravel_index": [[529, "unravel-index"]], "End-to-End Training Pipeline in Ivy": [[61, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[61, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[61, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[61, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[61, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[61, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[61, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[61, "Plotting-the-training-metrics"]], "Save the trained Model": [[61, "Save-the-trained-Model"]], "Data type": [[91, "module-ivy.data_classes.container.data_type"], [68, "module-ivy.data_classes.array.data_type"], [647, "data-type"], [385, "module-ivy.functional.ivy.experimental.data_type"]], "Creation": [[67, "module-ivy.data_classes.array.creation"], [90, "module-ivy.data_classes.container.creation"], [646, "creation"], [384, "creation"]], "HuggingFace Tensorflow DeiT": [[62, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[62, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "1.1: Framework Selection": [[51, "1.1:-Framework-Selection"]], "Unify": [[51, "Unify"], [52, "Unify"], [50, "Unify"], [38, "Unify"]], "Compile": [[51, "Compile"], [52, "Compile"], [50, "Compile"]], "Transpile": [[51, "Transpile"], [52, "Transpile"], [50, "Transpile"], [38, "Transpile"]], "1.2: As a Decorator": [[52, "1.2:-As-a-Decorator"]], "Compilation of a Basic Function": [[58, "Compilation-of-a-Basic-Function"]], "Installs \ud83d\udcbe": [[58, "Installs-\ud83d\udcbe"], [57, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[58, "Imports-\ud83d\udec3"], [57, "Imports-\ud83d\udec3"]], "Import Ivy compiler": [[58, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[58, "Function-compilation-\ud83d\udee0"]], "Set backend": [[58, "Set-backend"]], "Sample input": [[58, "Sample-input"]], "Define function to compile": [[58, "Define-function-to-compile"]], "Compile the function": [[58, "Compile-the-function"]], "Check results": [[58, "Check-results"], [58, "id1"]], "Compiling simple neural network \ud83e\udde0": [[58, "Compiling-simple-neural-network-\ud83e\udde0"]], "Define Model": [[58, "Define-Model"], [57, "Define-Model"]], "Create model": [[58, "Create-model"]], "Define input": [[58, "Define-input"]], "Compile network": [[58, "Compile-network"]], "0.1: Compile": [[48, "0.1:-Compile"]], "Basic Operations with Ivy": [[57, "Basic-Operations-with-Ivy"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[57, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[57, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[57, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[57, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[57, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[57, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[57, "Set-Backend-Framework"]], "Create Model": [[57, "Create-Model"]], "Create Optimizer": [[57, "Create-Optimizer"]], "Input and Target": [[57, "Input-and-Target"]], "Loss Function": [[57, "Loss-Function"]], "Training Loop": [[57, "Training-Loop"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[59, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[59, "Table-of-Contents"]], "Defining the model": [[59, "Defining-the-model"]], "Model construction": [[59, "Model-construction"]], "Some helper functions": [[59, "Some-helper-functions"]], "Transpiling the model": [[59, "Transpiling-the-model"]], "PyTorch pipeline": [[59, "PyTorch-pipeline"]], "Dataset download": [[59, "Dataset-download"]], "DataLoader": [[59, "DataLoader"]], "Training": [[59, "Training"]], "Device": [[69, 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Transpile Functions \ud83d\udd22": [[63, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[63, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[63, "3.-Transpile-Models-\ud83c\udf10"]], "1.0: Lazy vs Eager": [[50, "1.0:-Lazy-vs-Eager"]], "Resnet 18": [[64, "Resnet-18"]], "0.2: Transpile": [[49, "0.2:-Transpile"]], "0.0: Unify": [[47, "0.0:-Unify"]], "2.0: Kornia": [[54, "2.0:-Kornia"]], "1.3: Dynamic vs Static": [[53, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[53, "Dynamic"]], "Static": [[53, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[53, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Deepmind PerceiverIO on GPU": [[60, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[60, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[60, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[60, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[60, "Run-the-demo..."]], "\u2026with torch backend": [[60, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[60, "....with-tensorflow-backend"]], "\u2026with jax backend": [[60, "...with-jax-backend"]], "\u2026with numpy backend": [[60, "...with-numpy-backend"]], "unset_shape_array_mode": [[626, "unset-shape-array-mode"]], "Constants": [[644, "module-ivy.functional.ivy.constants"], [383, "module-ivy.functional.ivy.experimental.constants"]], "jac": [[636, "jac"]], "Meta": [[657, "meta"], [394, "module-ivy.functional.ivy.experimental.meta"]], "to_native_shape": [[614, "to-native-shape"]], "execute_with_gradients": [[633, "execute-with-gradients"]], "to_ivy_shape": [[612, "to-ivy-shape"]], "unset_exception_trace_mode": [[619, "unset-exception-trace-mode"]], "supports_inplace_updates": [[611, 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"put_along_axis": [[505, "put-along-axis"]], "pad_sequence": [[500, "pad-sequence"]], "expand": [[487, "expand"]], "partial_tensor_to_vec": [[502, "partial-tensor-to-vec"]], "top_k": [[510, "top-k"]], "fold": [[492, "fold"]], "take_along_axis": [[509, "take-along-axis"]], "take": [[508, "take"]], "atleast_2d": [[478, "atleast-2d"]], "column_stack": [[483, "column-stack"]], "fill_diagonal": [[488, "fill-diagonal"]], "vstack": [[516, "vstack"]], "as_strided": [[475, "as-strided"]], "associative_scan": [[476, "associative-scan"]], "log_poisson_loss": [[471, "log-poisson-loss"]], "dstack": [[486, "dstack"]], "flipud": [[491, "flipud"]], "partial_fold": [[501, "partial-fold"]], "hstack": [[495, "hstack"]], "check_scalar": [[481, "check-scalar"]], "partial_vec_to_tensor": [[504, "partial-vec-to-tensor"]], "moveaxis": [[498, "moveaxis"]], "concat_from_sequence": [[484, "concat-from-sequence"]], "dsplit": [[485, "dsplit"]], "rot90": [[506, "rot90"]], "smooth_l1_loss": [[473, "smooth-l1-loss"]], "rnn": [[436, "rnn"]], "khatri_rao": [[450, "khatri-rao"]], "eigvals": [[446, "eigvals"]], "hinge_embedding_loss": [[467, "hinge-embedding-loss"]], "kron": [[451, "kron"]], "cond": [[441, "cond"]], "diagflat": [[442, "diagflat"]], "multi_dot": [[458, "multi-dot"]], "tt_matrix_to_tensor": [[465, "tt-matrix-to-tensor"]], "interpolate": [[426, "interpolate"]], "mode_dot": [[457, "mode-dot"]], "truncated_svd": [[464, "truncated-svd"]], "tensor_train": [[463, "tensor-train"]], "tucker": [[466, "tucker"]], "lu_factor": [[453, "lu-factor"]], "solve_triangular": [[461, "solve-triangular"]], "sliding_window": [[437, "sliding-window"]], "dot": [[443, "dot"]], "multi_mode_dot": [[459, "multi-mode-dot"]], "interp": [[425, "interp"]], "eigh_tridiagonal": [[445, "eigh-tridiagonal"]], "pool": [[432, "pool"]], "lu_solve": [[454, "lu-solve"]], "svd_flip": [[462, "svd-flip"]], "make_svd_non_negative": [[455, "make-svd-non-negative"]], "adjoint": [[439, "adjoint"]], "ifft": [[423, "ifft"]], "initialize_tucker": [[449, "initialize-tucker"]], "higher_order_moment": [[448, "higher-order-moment"]], "max_pool2d": [[428, "max-pool2d"]], "nearest_interpolate": [[431, "nearest-interpolate"]], "ifftn": [[424, "ifftn"]], "kl_div": [[469, "kl-div"]], "kronecker": [[452, "kronecker"]], "max_unpool1d": [[430, "max-unpool1d"]], "stft": [[438, "stft"]], "max_pool1d": [[427, "max-pool1d"]], "rfft": [[434, "rfft"]], "max_pool3d": [[429, "max-pool3d"]], "matrix_exp": [[456, "matrix-exp"]], "huber_loss": [[468, "huber-loss"]], "reduce_window": [[433, "reduce-window"]], "rfftn": [[435, "rfftn"]], "partial_tucker": [[460, "partial-tucker"]], "general_inner_product": [[447, "general-inner-product"]], "batched_outer": [[440, "batched-outer"]], "random_cp": [[338, "random-cp"]], "lgamma": [[369, "lgamma"]], "modf": [[370, "modf"]], "unsorted_segment_sum": [[347, "unsorted-segment-sum"]], "lerp": [[368, "lerp"]], "frexp": [[363, "frexp"]], "polyval": [[337, "polyval"]], "tril_indices": [[343, "tril-indices"]], "conj": [[353, "conj"]], "kaiser_bessel_derived_window": [[332, "kaiser-bessel-derived-window"]], "hamming_window": [[329, "hamming-window"]], "kaiser_window": [[333, "kaiser-window"]], "mel_weight_matrix": [[334, "mel-weight-matrix"]], "random_tr": [[340, "random-tr"]], "allclose": [[349, "allclose"]], "hypot": [[365, "hypot"]], "binarizer": [[352, "binarizer"]], "digamma": [[357, "digamma"]], "nansum": [[371, "nansum"]], "random_parafac2": [[339, "random-parafac2"]], "signbit": [[373, "signbit"]], "diff": [[356, "diff"]], "amax": [[350, "amax"]], "count_nonzero": [[355, "count-nonzero"]], "unsorted_segment_mean": [[345, "unsorted-segment-mean"]], "ldexp": [[367, "ldexp"]], "indices": [[331, "indices"]], "sinc": [[374, "sinc"]], "vorbis_window": [[348, "vorbis-window"]], "hann_window": [[330, "hann-window"]], "nextafter": [[372, "nextafter"]], "ndindex": [[336, "ndindex"]], "random_tt": [[341, "random-tt"]], "ndenumerate": [[335, "ndenumerate"]], "unsorted_segment_min": [[346, "unsorted-segment-min"]], "copysign": [[354, "copysign"]], "sparsify_tensor": [[375, "sparsify-tensor"]], "fix": [[360, "fix"]], "random_tucker": [[342, "random-tucker"]], "gradient": [[364, "gradient"]], "isclose": [[366, "isclose"]], "trilu": [[344, "trilu"]], "erfc": [[358, "erfc"]], "amin": [[351, "amin"]], "float_power": [[361, "float-power"]], "fmax": [[362, "fmax"]], "erfinv": [[359, "erfinv"]], "Using TensorFlow Models in your PyTorch Projects": [[9, "Using-TensorFlow-Models-in-your-PyTorch-Projects"], [8, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[9, "Framework-Incompatibility"], [8, "Framework-Incompatibility"], [24, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[9, "Transpiling-a-TensorFlow-model-to-PyTorch"], [8, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[9, "About-the-transpiled-model"], [8, "About-the-transpiled-model"], [24, "About-the-transpiled-model"]], "Setting-up the source model": [[9, "Setting-up-the-source-model"], [8, "Setting-up-the-source-model"], [24, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[9, "Converting-the-model-from-TensorFlow-to-PyTorch"], [8, "Converting-the-model-from-TensorFlow-to-PyTorch"], [24, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[9, "Comparing-the-results"], [8, "Comparing-the-results"], [24, "Comparing-the-results"], [10, "Comparing-the-results"], [11, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[9, "Fine-tuning-the-transpiled-model"], [8, "Fine-tuning-the-transpiled-model"], [24, "Fine-tuning-the-transpiled-model"], [10, "Fine-tuning-the-transpiled-model"], [11, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[9, "Conclusion"], [8, "Conclusion"], [24, "Conclusion"], [10, "Conclusion"], [11, "Conclusion"]], "Transpiling a Tensorflow model to build on top": [[30, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Transpiling Functions from PyTorch to TensorFlow": [[43, "Transpiling-Functions-from-PyTorch-to-TensorFlow"]], "Image Segmentation with Ivy UNet": [[13, "Image-Segmentation-with-Ivy-UNet"], [12, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[13, "Imports"], [27, "Imports"], [22, "Imports"], [23, "Imports"], [12, "Imports"]], "Data Preparation": [[13, "Data-Preparation"], [5, "Data-Preparation"], [4, "Data-Preparation"], [22, "Data-Preparation"], [23, "Data-Preparation"], [6, "Data-Preparation"], [12, "Data-Preparation"], [7, "Data-Preparation"]], "Custom Preprocessing": [[13, "Custom-Preprocessing"], [12, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[13, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [22, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [23, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[13, "Visualise-image"], [22, "Visualise-image"], [23, "Visualise-image"], [12, "Visualise-image"]], "Model Inference": [[13, "Model-Inference"], [12, "Model-Inference"]], "Initializing Native Torch UNet": [[13, "Initializing-Native-Torch-UNet"], [12, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[13, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"], [12, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[13, "Custom-masking-function"], [12, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[13, "Use-the-model-to-segment-your-images-\ud83d\ude80"], [12, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[13, "TensorFlow-backend"], [12, "TensorFlow-backend"]], "JAX": [[13, "JAX"], [12, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[13, "Appendix:-the-Ivy-native-implementation-of-UNet"], [12, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Tutorials And Examples": [[32, "tutorials-and-examples"]], "Learn the basics": [[32, "learn-the-basics"], [33, "learn-the-basics"]], "Examples and Demos": [[32, "examples-and-demos"], [3, "examples-and-demos"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "How to use decorators": [[39, "How-to-use-decorators"]], "Trace": [[39, "Trace"], [38, "Trace"]], "Graph Transpile": [[39, "Graph-Transpile"]], "Transpile \ud83d\udea7": [[39, "Transpile-\ud83d\udea7"]], "Developing a convolutional network using Ivy": [[31, "Developing-a-convolutional-network-using-Ivy"]], "Ivy AlexNet demo": [[5, "Ivy-AlexNet-demo"], [4, "Ivy-AlexNet-demo"]], "Installation": [[5, "Installation"], [4, "Installation"], [24, "Installation"], [22, "Installation"], [23, "Installation"]], "Ivy AlexNet inference in Torch": [[5, "Ivy-AlexNet-inference-in-Torch"], [4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[5, "TensorFlow-inference"], [4, "TensorFlow-inference"]], "JAX inference": [[5, "JAX-inference"], [4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[5, "Appendix-(Ivy-code-for-AlexNet-implementation)"], [4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "Accelerating PyTorch models with JAX": [[25, "Accelerating-PyTorch-models-with-JAX"], [26, "Accelerating-PyTorch-models-with-JAX"]], "Transpile code": [[37, "Transpile-code"]], "Training PyTorch ResNet in your TensorFlow Projects": [[24, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Transpiling a PyTorch model to TensorFlow": [[24, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "Load the Data": [[24, "Load-the-Data"]], "Visualize a few images": [[24, "Visualize-a-few-images"]], "Load the pre-trained model": [[24, "Load-the-pre-trained-model"]], "Transpile any model": [[41, "Transpile-any-model"]], "Round up": [[41, "Round-up"]], "Accelerating XGBoost with JAX": [[27, "Accelerating-XGBoost-with-JAX"]], "Tests": [[27, "Tests"]], "Loading the Data": [[27, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[27, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[27, "JAX-backend"]], "Tensorflow backend": [[27, "Tensorflow-backend"]], "PyTorch backend": [[27, "PyTorch-backend"]], "More exhaustive example": [[27, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[27, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[27, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[27, "Comparison-of-Metrics"]], "Write Ivy code": [[34, "Write-Ivy-code"]], "Contents": [[34, "Contents"]], "Installing Ivy": [[34, "Installing-Ivy"]], "Importing Ivy": [[34, "Importing-Ivy"], [0, "Importing-Ivy"]], "Ivy Backend Handler": [[34, "Ivy-Backend-Handler"], [45, "Ivy-Backend-Handler"]], "Data Structures": [[34, "Data-Structures"], [45, "Data-Structures"]], "Ivy Functional API": [[34, "Ivy-Functional-API"], [45, "Ivy-Functional-API"]], "Ivy Stateful API": [[34, "Ivy-Stateful-API"], [45, "Ivy-Stateful-API"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Using Ivy ResNet": [[22, "Using-Ivy-ResNet"], [23, "Using-Ivy-ResNet"]], "Prepare the set of labels": [[22, "Prepare-the-set-of-labels"], [23, "Prepare-the-set-of-labels"]], "Model Inference ResNet34": [[22, "Model-Inference-ResNet34"], [23, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[22, "Initializing-Native-Torch-ResNet34"], [23, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[22, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"], [23, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[22, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [22, "id1"], [23, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [23, "id1"]], "Model Inference ResNet50": [[22, "Model-Inference-ResNet50"], [23, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[22, "Initializing-Native-Torch-ResNet50"], [23, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[22, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"], [23, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "Transpiling a PyTorch model to build on top": [[28, "Transpiling-a-PyTorch-model-to-build-on-top"]], "# Ivy Bert Demo": [[6, "#-Ivy-Bert-Demo"], [7, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[6, "Install-the-dependecies"], [7, "Install-the-dependecies"]], "Import the modules": [[6, "Import-the-modules"], [7, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[6, "Ivy-inference-with-Sequence-Classification"], [7, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[6, "Ivy-model-inference-with-tensorflow"], [7, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[6, "Ivy-model-inference-with-Jax"], [7, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[6, "Ivy-model-inference-with-torch"], [7, "Ivy-model-inference-with-torch"]], "Trace code": [[36, "Trace-code"]], "Accelerating MMPreTrain models with JAX": [[20, "Accelerating-MMPreTrain-models-with-JAX"], [21, "Accelerating-MMPreTrain-models-with-JAX"]], "Write a model using Ivy": [[42, "Write-a-model-using-Ivy"]], "ODSC Ivy Demo": [[45, "ODSC-Ivy-Demo"]], "Ivy as a Framework": [[45, "Ivy-as-a-Framework"]], "Graph Tracer": [[45, "Graph-Tracer"]], "Any function": [[45, "Any-function"], [46, "Any-function"]], "Any library": [[45, "Any-library"], [46, "Any-library"]], "Any model": [[45, "Any-model"], [46, "Any-model"]], "How To Convert Models from PyTorch to PaddlePaddle": [[10, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"], [11, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[10, "About-the-Model"], [11, "About-the-Model"]], "Transpiling the Model": [[10, "Transpiling-the-Model"], [11, "Transpiling-the-Model"]], "Transpiling a haiku model to build on top": [[29, "Transpiling-a-haiku-model-to-build-on-top"]], "Unify code": [[35, "Unify-code"]], "Quickstart": [[46, "Quickstart"]], "Get familiar with Ivy": [[46, "Get-familiar-with-Ivy"]], "Functional API": [[46, "Functional-API"]], "Stateful API": [[46, "Stateful-API"]], "Tracing code": [[46, "Tracing-code"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Transpiling Models from PyTorch to TensorFlow": [[44, "Transpiling-Models-from-PyTorch-to-TensorFlow"]], "Transpile any library": [[40, "Transpile-any-library"]], "Lazy vs Eager": [[38, "Lazy-vs-Eager"]], "promote_types": [[193, "promote-types"]], "is_int_dtype": [[190, "is-int-dtype"]], "unset_soft_device_mode": [[233, "unset-soft-device-mode"]], "unset_default_float_dtype": [[204, "unset-default-float-dtype"]], "is_float_dtype": [[188, "is-float-dtype"]], "set_default_dtype": [[197, "set-default-dtype"]], "gpu_is_available": [[217, "gpu-is-available"]], "to_device": [[229, "to-device"]], "dev": [[212, "dev"]], "percent_used_mem_on_dev": [[222, "percent-used-mem-on-dev"]], "set_default_device": [[224, "set-default-device"]], "promote_types_of_inputs": [[194, "promote-types-of-inputs"]], "unset_default_device": [[232, "unset-default-device"]], "set_default_uint_dtype": [[200, "set-default-uint-dtype"]], "print_all_ivy_arrays_on_dev": [[223, "print-all-ivy-arrays-on-dev"]], "set_default_int_dtype": [[199, "set-default-int-dtype"]], "unset_default_uint_dtype": [[206, 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\ No newline at end of file