code: def ctc_loss_and_grad(
      logits, labels, label_length, logit_length, unique=None
  ):
      """Computes the CTC loss and gradients.
  
      Most users will want fwd_bwd.ctc_loss
  
      This function returns the computed gradient, it does not have a gradient
      of its own defined.
  
      Args:
        logits: tensor of shape [frames, batch_size, num_labels]
        labels: tensor of shape [batch_size, max_label_seq_length]
        label_length: tensor of shape [batch_size] Length of reference label
          sequence in labels.
        logit_length: tensor of shape [batch_size] Length of input sequence in
          logits.
        unique: (optional) unique label indices as computed by unique(labels) If
          supplied, enables an implementation that is faster and more memory
          efficient on TPU.
  
      Returns:
        loss: tensor of shape [batch_size]
        gradient: tensor of shape [frames, batch_size, num_labels]
      """
  
      num_labels = _get_dim(logits, 2)
      max_label_seq_length = _get_dim(labels, 1)
  
      ilabel_log_probs = nn_ops.log_softmax(logits)
      state_log_probs = _ilabel_to_state(
          labels, num_labels, ilabel_log_probs
      )
      state_trans_probs = _ctc_state_trans(labels)
      (
          initial_state_log_probs,
          final_state_log_probs,
      ) = ctc_state_log_probs(label_length, max_label_seq_length)
      fwd_bwd_log_probs, log_likelihood = _forward_backward_log(
          state_trans_log_probs=math_ops.log(state_trans_probs),
          initial_state_log_probs=initial_state_log_probs,
          final_state_log_probs=final_state_log_probs,
          observed_log_probs=state_log_probs,
          sequence_length=logit_length,
      )
  
      if unique:
          olabel_log_probs = _state_to_olabel_unique(
              labels, num_labels, fwd_bwd_log_probs, unique
          )
      else:
          olabel_log_probs = _state_to_olabel(
              labels, num_labels, fwd_bwd_log_probs
          )
  
      grad = math_ops.exp(ilabel_log_probs) - math_ops.exp(
          olabel_log_probs
      )
  
      # Applies the sequence mask for the gradient. It is enough to appply the mask
      # only for ilabel_log_probs because olabel_log_probs already consider the
      # mask. However, it is just safe and clean to apply it for the gradient.
      max_logit_length = _get_dim(logits, 0)
      logit_mask = array_ops.sequence_mask(
          logit_length, max_logit_length, dtypes.float32
      )
      logit_mask = array_ops.transpose(logit_mask, perm=[1, 0])
      logit_mask = array_ops.expand_dims(logit_mask, axis=2)
      grad *= logit_mask
  
      loss = -log_likelihood
      return loss, grad
  
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 16 for calling a global function with example usage: ctc_loss_and_grad(logits, labels, label_length, logit_length, unique) and returns: loss,  grad