message: [TF CTC] Fix Returns documentation stanzas to be properly formatted (see https://www.tensorflow.org/api_docs/python/tf/nn/ctc_greedy_decoder)
diff: @@ -212,14 +212,19 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True):
  Returns:
  A tuple `(decoded, neg_sum_logits)` where
  +
  decoded: A single-element list. `decoded[0]`
  is an `SparseTensor` containing the decoded outputs s.t.:
  +
  `decoded.indices`: Indices matrix `(total_decoded_outputs, 2)`.
  The rows store: `[batch, time]`.
  +
  `decoded.values`: Values vector, size `(total_decoded_outputs)`.
  The vector stores the decoded classes.
  +
  `decoded.dense_shape`: Shape vector, size `(2)`.
  The shape values are: `[batch_size, max_decoded_length]`
  +
  neg_sum_logits: A `float` matrix `(batch_size x 1)` containing, for the
  sequence found, the negative of the sum of the greatest logit at each
  timeframe.
  @@ -259,14 +264,19 @@ def ctc_beam_search_decoder(inputs, sequence_length, beam_width=100,
  Returns:
  A tuple `(decoded, log_probabilities)` where
  +
  decoded: A list of length top_paths, where `decoded[j]`
  is a `SparseTensor` containing the decoded outputs:
  +
  `decoded[j].indices`: Indices matrix `(total_decoded_outputs[j] x 2)`
  The rows store: [batch, time].
  +
  `decoded[j].values`: Values vector, size `(total_decoded_outputs[j])`.
  The vector stores the decoded classes for beam j.
  +
  `decoded[j].dense_shape`: Shape vector, size `(2)`.
  The shape values are: `[batch_size, max_decoded_length[j]]`.
  +
  log_probability: A `float` matrix `(batch_size x top_paths)` containing
  sequence log-probabilities.
  """
  @@ -301,14 +311,19 @@ def ctc_beam_search_decoder_v2(inputs, sequence_length, beam_width=100,
  Returns:
  A tuple `(decoded, log_probabilities)` where
  +
  decoded: A list of length top_paths, where `decoded[j]`
  is a `SparseTensor` containing the decoded outputs:
  +
  `decoded[j].indices`: Indices matrix `[total_decoded_outputs[j], 2]`;
  The rows store: `[batch, time]`.
  +
  `decoded[j].values`: Values vector, size `[total_decoded_outputs[j]]`.
  The vector stores the decoded classes for beam `j`.
  +
  `decoded[j].dense_shape`: Shape vector, size `(2)`.
  The shape values are: `[batch_size, max_decoded_length[j]]`.
  +
  log_probability: A `float` matrix `[batch_size, top_paths]` containing
  sequence log-probabilities.
  """
  