parameters: (inputs,
                                 sequence_length,
                                 beam_width=100,
                                 top_paths=1)
return_statement: return ctc_beam_search_decoder(
        inputs,
        sequence_length=sequence_length,
        beam_width=beam_width,
        top_paths=top_paths,
        merge_repeated=False)
docstring: Performs beam search decoding on the logits given in input.
  
    **Note** The `ctc_greedy_decoder` is a special case of the
    `ctc_beam_search_decoder` with `top_paths=1` and `beam_width=1` (but
    that decoder is faster for this special case).
  
    Args:
      inputs: 3-D `float` `Tensor`, size `[max_time, batch_size, num_classes]`.
        The logits.
      sequence_length: 1-D `int32` vector containing sequence lengths, having size
        `[batch_size]`.
      beam_width: An int scalar >= 0 (beam search beam width).
      top_paths: An int scalar >= 0, <= beam_width (controls output size).
  
    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.
docstring_summary: Performs beam search decoding on the logits given in input.
docstring_tokens: Performs beam search decoding on the logits given in input .
function: def ctc_beam_search_decoder_v2(inputs,
                                 sequence_length,
                                 beam_width=100,
                                 top_paths=1):
    """Performs beam search decoding on the logits given in input.
  
    **Note** The `ctc_greedy_decoder` is a special case of the
    `ctc_beam_search_decoder` with `top_paths=1` and `beam_width=1` (but
    that decoder is faster for this special case).
  
    Args:
      inputs: 3-D `float` `Tensor`, size `[max_time, batch_size, num_classes]`.
        The logits.
      sequence_length: 1-D `int32` vector containing sequence lengths, having size
        `[batch_size]`.
      beam_width: An int scalar >= 0 (beam search beam width).
      top_paths: An int scalar >= 0, <= beam_width (controls output size).
  
    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.
    """
  
    # Note, merge_repeated is an invalid optimization that is removed from the
    # public API: it returns low probability paths.
    return ctc_beam_search_decoder(
        inputs,
        sequence_length=sequence_length,
        beam_width=beam_width,
        top_paths=top_paths,
        merge_repeated=False)
function_tokens: def ctc_beam_search_decoder_v2 ( inputs , sequence_length , beam_width = 100 , top_paths = 1 ) : # Note, merge_repeated is an invalid optimization that is removed from the # public API: it returns low probability paths. return ctc_beam_search_decoder ( inputs , sequence_length = sequence_length , beam_width = beam_width , top_paths = top_paths , merge_repeated = False )