Fix misspells. (#10000)
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f488419cd6
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eac1a7bade
@ -65,7 +65,7 @@ class ResampleTest(test.TestCase):
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self.assertAllEqual([compat.as_bytes(str(c))
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for c in returned_classes], returned_data)
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total_returned = len(returned_classes)
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# Subsampling rejects a large precentage of the initial data in
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# Subsampling rejects a large percentage of the initial data in
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# this case.
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self.assertGreater(total_returned, 20000 * 0.2)
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class_counts = np.array([
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@ -166,7 +166,7 @@ class ShapesFromLocAndScaleTest(test.TestCase):
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batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
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loc, scale)
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# batch_shape depends on both args, and so is dynamic. Since loc did not
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# have static shape, we infered event shape entirely from scale, and this
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# have static shape, we inferred event shape entirely from scale, and this
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# is available statically.
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self.assertAllEqual(
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[5, 2], batch_shape.eval(feed_dict={loc: np.zeros((2, 3))}))
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@ -188,7 +188,7 @@ class ParallelMapDatasetOp : public OpKernel {
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if (!output_buffer_.empty() && output_buffer_.front().is_produced) {
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// A new output element is available. Forward the status
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// from computing it, and (if we sucessfully got an
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// from computing it, and (if we successfully got an
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// element) the output values.
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Status s = output_buffer_.front().output_status;
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if (s.ok()) {
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