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update all tutorial nbs headers to new header
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‎docs/callback.core.html‎

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<pre>/Users/nacho/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/errors.py:154: UserWarning: Insufficiently recent colorama version found. Numba requires colorama &gt;= 0.3.9
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<pre>/Users/nacho/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/errors.py:154: UserWarning: Insufficiently recent colorama version found. Numba requires colorama &gt;= 0.3.9
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<pre>/Users/nacho/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/errors.py:154: UserWarning: Insufficiently recent colorama version found. Numba requires colorama &gt;= 0.3.9
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<pre>/Users/nacho/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/errors.py:154: UserWarning: Insufficiently recent colorama version found. Numba requires colorama &gt;= 0.3.9
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<pre>/Users/nacho/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/errors.py:154: UserWarning: Insufficiently recent colorama version found. Numba requires colorama &gt;= 0.3.9
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‎docs/learner.html‎

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<h4 id="tsimage_learner" class="doc_header"><code>tsimage_learner</code><a href="https://github.com/timeseriesAI/tsai/tree/main/tsai/learner.py#L246" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>tsimage_learner</code>(<strong><code>dls</code></strong>, <strong><code>arch</code></strong>=<em><code>None</code></em>, <strong><code>pretrained</code></strong>=<em><code>False</code></em>, <strong><code>loss_func</code></strong>=<em><code>None</code></em>, <strong><code>opt_func</code></strong>=<em><code>Adam</code></em>, <strong><code>lr</code></strong>=<em><code>0.001</code></em>, <strong><code>cbs</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>None</code></em>, <strong><code>path</code></strong>=<em><code>None</code></em>, <strong><code>model_dir</code></strong>=<em><code>'models'</code></em>, <strong><code>wd</code></strong>=<em><code>None</code></em>, <strong><code>wd_bn_bias</code></strong>=<em><code>False</code></em>, <strong><code>train_bn</code></strong>=<em><code>True</code></em>, <strong><code>moms</code></strong>=<em><code>(0.95, 0.85, 0.95)</code></em>, <strong><code>c_in</code></strong>=<em><code>None</code></em>, <strong><code>c_out</code></strong>=<em><code>None</code></em>, <strong><code>device</code></strong>=<em><code>None</code></em>, <strong><code>verbose</code></strong>=<em><code>False</code></em>, <strong><code>init</code></strong>=<em><code>None</code></em>, <strong><code>p</code></strong>=<em><code>0.0</code></em>, <strong><code>n_out</code></strong>=<em><code>1000</code></em>, <strong><code>stem_szs</code></strong>=<em><code>(32, 32, 64)</code></em>, <strong><code>widen</code></strong>=<em><code>1.0</code></em>, <strong><code>sa</code></strong>=<em><code>False</code></em>, <strong><code>act_cls</code></strong>=<em><code>ReLU</code></em>, <strong><code>ndim</code></strong>=<em><code>2</code></em>, <strong><code>ks</code></strong>=<em><code>3</code></em>, <strong><code>stride</code></strong>=<em><code>2</code></em>, <strong><code>groups</code></strong>=<em><code>1</code></em>, <strong><code>reduction</code></strong>=<em><code>None</code></em>, <strong><code>nh1</code></strong>=<em><code>None</code></em>, <strong><code>nh2</code></strong>=<em><code>None</code></em>, <strong><code>dw</code></strong>=<em><code>False</code></em>, <strong><code>g2</code></strong>=<em><code>1</code></em>, <strong><code>sym</code></strong>=<em><code>False</code></em>, <strong><code>norm_type</code></strong>=<em><code>&lt;NormType.Batch: 1&gt;</code></em>, <strong><code>pool</code></strong>=<em><code>AvgPool</code></em>, <strong><code>pool_first</code></strong>=<em><code>True</code></em>, <strong><code>padding</code></strong>=<em><code>None</code></em>, <strong><code>bias</code></strong>=<em><code>None</code></em>, <strong><code>bn_1st</code></strong>=<em><code>True</code></em>, <strong><code>transpose</code></strong>=<em><code>False</code></em>, <strong><code>xtra</code></strong>=<em><code>None</code></em>, <strong><code>bias_std</code></strong>=<em><code>0.01</code></em>, <strong><code>dilation</code></strong>:<code>Union</code>[<code>int</code>, <code>Tuple</code>[<code>int</code>, <code>int</code>]]=<em><code>1</code></em>, <strong><code>padding_mode</code></strong>:<code>str</code>=<em><code>'zeros'</code></em>, <strong><code>dtype</code></strong>=<em><code>None</code></em>)</p>
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<h4 id="tsimage_learner" class="doc_header"><code>tsimage_learner</code><a href="https://github.com/timeseriesAI/tsai/tree/main/tsai/learner.py#L246" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>tsimage_learner</code>(<strong><code>dls</code></strong>, <strong><code>arch</code></strong>=<em><code>None</code></em>, <strong><code>pretrained</code></strong>=<em><code>False</code></em>, <strong><code>loss_func</code></strong>=<em><code>None</code></em>, <strong><code>opt_func</code></strong>=<em><code>Adam</code></em>, <strong><code>lr</code></strong>=<em><code>0.001</code></em>, <strong><code>cbs</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>None</code></em>, <strong><code>path</code></strong>=<em><code>None</code></em>, <strong><code>model_dir</code></strong>=<em><code>'models'</code></em>, <strong><code>wd</code></strong>=<em><code>None</code></em>, <strong><code>wd_bn_bias</code></strong>=<em><code>False</code></em>, <strong><code>train_bn</code></strong>=<em><code>True</code></em>, <strong><code>moms</code></strong>=<em><code>(0.95, 0.85, 0.95)</code></em>, <strong><code>c_in</code></strong>=<em><code>None</code></em>, <strong><code>c_out</code></strong>=<em><code>None</code></em>, <strong><code>device</code></strong>=<em><code>None</code></em>, <strong><code>verbose</code></strong>=<em><code>False</code></em>, <strong><code>init</code></strong>=<em><code>None</code></em>, <strong><code>p</code></strong>=<em><code>0.0</code></em>, <strong><code>n_out</code></strong>=<em><code>1000</code></em>, <strong><code>stem_szs</code></strong>=<em><code>(32, 32, 64)</code></em>, <strong><code>widen</code></strong>=<em><code>1.0</code></em>, <strong><code>sa</code></strong>=<em><code>False</code></em>, <strong><code>act_cls</code></strong>=<em><code>ReLU</code></em>, <strong><code>ndim</code></strong>=<em><code>2</code></em>, <strong><code>ks</code></strong>=<em><code>3</code></em>, <strong><code>stride</code></strong>=<em><code>2</code></em>, <strong><code>groups</code></strong>=<em><code>1</code></em>, <strong><code>reduction</code></strong>=<em><code>None</code></em>, <strong><code>nh1</code></strong>=<em><code>None</code></em>, <strong><code>nh2</code></strong>=<em><code>None</code></em>, <strong><code>dw</code></strong>=<em><code>False</code></em>, <strong><code>g2</code></strong>=<em><code>1</code></em>, <strong><code>sym</code></strong>=<em><code>False</code></em>, <strong><code>norm_type</code></strong>=<em><code>&lt;NormType.Batch: 1&gt;</code></em>, <strong><code>pool</code></strong>=<em><code>AvgPool</code></em>, <strong><code>pool_first</code></strong>=<em><code>True</code></em>, <strong><code>padding</code></strong>=<em><code>None</code></em>, <strong><code>bias</code></strong>=<em><code>None</code></em>, <strong><code>bn_1st</code></strong>=<em><code>True</code></em>, <strong><code>transpose</code></strong>=<em><code>False</code></em>, <strong><code>xtra</code></strong>=<em><code>None</code></em>, <strong><code>bias_std</code></strong>=<em><code>0.01</code></em>, <strong><code>dilation</code></strong>:<code>Tuple[~T, ~T]]</code>[<code>int</code>]=<em><code>1</code></em>, <strong><code>padding_mode</code></strong>:<code>str</code>=<em><code>'zeros'</code></em>, <strong><code>dtype</code></strong>=<em><code>None</code></em>)</p>
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<pre>/Users/nacho/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/errors.py:154: UserWarning: Insufficiently recent colorama version found. Numba requires colorama &gt;= 0.3.9
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<pre>/Users/nacho/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/errors.py:154: UserWarning: Insufficiently recent colorama version found. Numba requires colorama &gt;= 0.3.9
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‎docs/models.ResNet.html‎

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<h2 id="ResBlock" class="doc_header"><code>class</code> <code>ResBlock</code><a href="https://github.com/fastai/fastai/tree/master/fastai/layers.py#L461" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>ResBlock</code>(<strong><code>expansion</code></strong>, <strong><code>ni</code></strong>, <strong><code>nf</code></strong>, <strong><code>stride</code></strong>=<em><code>1</code></em>, <strong><code>groups</code></strong>=<em><code>1</code></em>, <strong><code>reduction</code></strong>=<em><code>None</code></em>, <strong><code>nh1</code></strong>=<em><code>None</code></em>, <strong><code>nh2</code></strong>=<em><code>None</code></em>, <strong><code>dw</code></strong>=<em><code>False</code></em>, <strong><code>g2</code></strong>=<em><code>1</code></em>, <strong><code>sa</code></strong>=<em><code>False</code></em>, <strong><code>sym</code></strong>=<em><code>False</code></em>, <strong><code>norm_type</code></strong>=<em><code>&lt;NormType.Batch: 1&gt;</code></em>, <strong><code>act_cls</code></strong>=<em><code>ReLU</code></em>, <strong><code>ndim</code></strong>=<em><code>2</code></em>, <strong><code>ks</code></strong>=<em><code>3</code></em>, <strong><code>pool</code></strong>=<em><code>AvgPool</code></em>, <strong><code>pool_first</code></strong>=<em><code>True</code></em>, <strong><code>padding</code></strong>=<em><code>None</code></em>, <strong><code>bias</code></strong>=<em><code>None</code></em>, <strong><code>bn_1st</code></strong>=<em><code>True</code></em>, <strong><code>transpose</code></strong>=<em><code>False</code></em>, <strong><code>init</code></strong>=<em><code>'auto'</code></em>, <strong><code>xtra</code></strong>=<em><code>None</code></em>, <strong><code>bias_std</code></strong>=<em><code>0.01</code></em>, <strong><code>dilation</code></strong>:<code>Union</code>[<code>int</code>, <code>Tuple</code>[<code>int</code>, <code>int</code>]]=<em><code>1</code></em>, <strong><code>padding_mode</code></strong>:<code>str</code>=<em><code>'zeros'</code></em>, <strong><code>device</code></strong>=<em><code>None</code></em>, <strong><code>dtype</code></strong>=<em><code>None</code></em>) :: <code>Module</code></p>
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<h2 id="ResBlock" class="doc_header"><code>class</code> <code>ResBlock</code><a href="https://github.com/fastai/fastai/tree/master/fastai/layers.py#L461" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>ResBlock</code>(<strong><code>expansion</code></strong>, <strong><code>ni</code></strong>, <strong><code>nf</code></strong>, <strong><code>stride</code></strong>=<em><code>1</code></em>, <strong><code>groups</code></strong>=<em><code>1</code></em>, <strong><code>reduction</code></strong>=<em><code>None</code></em>, <strong><code>nh1</code></strong>=<em><code>None</code></em>, <strong><code>nh2</code></strong>=<em><code>None</code></em>, <strong><code>dw</code></strong>=<em><code>False</code></em>, <strong><code>g2</code></strong>=<em><code>1</code></em>, <strong><code>sa</code></strong>=<em><code>False</code></em>, <strong><code>sym</code></strong>=<em><code>False</code></em>, <strong><code>norm_type</code></strong>=<em><code>&lt;NormType.Batch: 1&gt;</code></em>, <strong><code>act_cls</code></strong>=<em><code>ReLU</code></em>, <strong><code>ndim</code></strong>=<em><code>2</code></em>, <strong><code>ks</code></strong>=<em><code>3</code></em>, <strong><code>pool</code></strong>=<em><code>AvgPool</code></em>, <strong><code>pool_first</code></strong>=<em><code>True</code></em>, <strong><code>padding</code></strong>=<em><code>None</code></em>, <strong><code>bias</code></strong>=<em><code>None</code></em>, <strong><code>bn_1st</code></strong>=<em><code>True</code></em>, <strong><code>transpose</code></strong>=<em><code>False</code></em>, <strong><code>init</code></strong>=<em><code>'auto'</code></em>, <strong><code>xtra</code></strong>=<em><code>None</code></em>, <strong><code>bias_std</code></strong>=<em><code>0.01</code></em>, <strong><code>dilation</code></strong>:<code>Tuple[~T, ~T]]</code>[<code>int</code>]=<em><code>1</code></em>, <strong><code>padding_mode</code></strong>:<code>str</code>=<em><code>'zeros'</code></em>, <strong><code>device</code></strong>=<em><code>None</code></em>, <strong><code>dtype</code></strong>=<em><code>None</code></em>) :: <code>Module</code></p>
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<p>Resnet block from <code>ni</code> to <code>nh</code> with <code>stride</code></p>
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