code: def ctc_loss(
      target,
      output,
      target_length,
      output_length,
      mask_index=0,
  ):
      """Runs CTC (Connectionist Temporal Classification) loss on each
      batch element.
  
      Arguments:
          target: Tensor `(batch_size, max_length)` containing the
              target sequences in integer format.
          output: Tensor `(batch_size, max_length, num_classes)`
              containing the output of the softmax.
          target_length: Tensor `(batch_size,)` containing the sequence length
              for each target sequence in the batch.
          output_length: Tensor `(batch_size,)` containing the sequence length
              for each output sequence in the batch.
          mask_index: The value in `target` and `output` that represents the
              blank label.
  
      Returns:
          A tensor of shape `(batch_size,)` containing the CTC loss for each
          sample in the batch.
      """
      target = tf.convert_to_tensor(target)
      target = tf.cast(target, dtype="int32")
      output = tf.convert_to_tensor(output)
      output = tf.cast(output, dtype="float32")
      return tf.nn.ctc_loss(
          labels=target,
          logits=output,
          label_length=target_length,
          logit_length=output_length,
          blank_index=mask_index,
          logits_time_major=False,
      )
  
imports: import warnings
  import tensorflow
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_output_shape
  from keras.backend.config import epsilon
  from keras.backend.tensorflow.core import cast
  from keras.backend.tensorflow.core import convert_to_tensor
label_desc: Use image node_id 36 for calling a global function with example usage: ctc_loss(target, output, target_length, output_length, mask_index) and returns: tf