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Merge branch 'master' into patch-2
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beginner_source/blitz/neural_networks_tutorial.py

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@@ -176,7 +176,7 @@ def num_flat_features(self, x):
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# -> loss
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#
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# So, when we call ``loss.backward()``, the whole graph is differentiated
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# w.r.t. the loss, and all Tensors in the graph that has ``requires_grad=True``
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# w.r.t. the loss, and all Tensors in the graph that have ``requires_grad=True``
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# will have their ``.grad`` Tensor accumulated with the gradient.
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#
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# For illustration, let us follow a few steps backward:

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