code: def ctc_greedy_decoder(
      inputs, sequence_length, merge_repeated=True, blank_index=None
  ):
      """Performs greedy decoding on the logits given in input (best path).
  
      Given a tensor as `inputs`, the `blank_index` parameter defines the class
      index of the blank symbol.
  
      For example:
  
      If `blank_index` is equal to 1:
  
      >>> inf = float("inf")
      >>> logits = tf.constant([[[   0., -inf, -inf],
      ...                        [ -2.3, -inf, -0.1]],
      ...                       [[ -inf, -0.5, -inf],
      ...                        [ -inf, -inf, -0.1]],
      ...                       [[ -inf, -inf, -inf],
      ...                        [ -0.1, -inf, -2.3]]])
      >>> seq_lens = tf.constant([2, 3])
      >>> outputs = tf.nn.ctc_greedy_decoder(
      ...     logits,
      ...     seq_lens,
      ...     blank_index=1)
  
      Notes:
  
      - Unlike `ctc_beam_search_decoder`, `ctc_greedy_decoder` considers blanks
        as regular elements when computing the probability of a sequence.
      - Default `blank_index` is `(num_classes - 1)`, unless overriden.
  
      If `merge_repeated` is `True`, merge repeated classes in output.
      This means that if consecutive logits' maximum indices are the same,
      only the first of these is emitted.  The sequence `A B B * B * B` (where '*'
      is the blank label) becomes
  
        * `A B B B` if `merge_repeated=True`.
        * `A B B B B` if `merge_repeated=False`.
  
      Args:
        inputs: 3-D `float` `Tensor` sized `[max_time, batch_size, num_classes]`.
          The logits.
        sequence_length: 1-D `int32` vector containing sequence lengths, having size
          `[batch_size]`.
        merge_repeated: Boolean.  Default: True.
        blank_index: (Optional). Default: `num_classes - 1`. Define the class index
          to use for the blank label. Negative values will start from num_classes,
          ie, -1 will reproduce the ctc_greedy_decoder behavior of using
          num_classes - 1 for the blank symbol, which corresponds to the default.
  
      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.
      """
  
      outputs = gen_ctc_ops.ctc_greedy_decoder(
          inputs,
          sequence_length,
          merge_repeated=merge_repeated,
          blank_index=blank_index,
      )
      (
          decoded_ix,
          decoded_val,
          decoded_shape,
          log_probabilities,
      ) = outputs
      return (
          [
              sparse_tensor.SparseTensor(
                  decoded_ix, decoded_val, decoded_shape
              )
          ],
          log_probabilities,
      )
  
imports: import uuid
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import device
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import custom_gradient
  from tensorflow.python.ops import functional_ops
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import gen_ctc_ops
  from tensorflow.python.ops import inplace_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import map_fn
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops.nn_grad import _BroadcastMul
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling a global function with example usage: ctc_greedy_decoder(inputs, sequence_length, merge_repeated, blank_index) and returns: list,  log_probabilities