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Fix documentation.
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@@ -88,9 +88,7 @@ def train_cnn_and_compute_accuracy(hyperparameters, train_images, train_labels,
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return validation_accuracy
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```
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The only difference is that we added the `@ray.remote` decorator specifying a
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little bit of type information (the input is a dictionary along with some numpy
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arrays, and the return value is a float).
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The only difference is that we added the `@ray.remote` decorator.
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Now a call to `train_cnn_and_compute_accuracy` does not execute the function. It
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submits the task to the scheduler and returns an object ID for the output
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