code: def ctc_loss(
      target,
      output,
      target_length,
      output_length,
      mask_index=0,
  ):
      batch_size, _, _ = output.shape
      batch_size, max_target_length = target.shape
  
      output = output.transpose((1, 0, 2))
      target = target.transpose((1, 0)).astype("int32")
  
      logits = jnn.log_softmax(output)
      mgrid_t, mgrid_b = jnp.meshgrid(
          jnp.arange(max_target_length), jnp.arange(batch_size)
      )
      logprobs_emit = logits[mgrid_t, mgrid_b, target[:, :, None]]
      logprobs_mask = logits[:, :, mask_index]
  
      logit_paddings = jnp.array(
          jnp.arange(max_target_length) < output_length[:, None],
          dtype=jnp.float32,
      )
  
      repeat = jnp.array(target[1:] == target[:-1])
      repeat = jnp.pad(repeat, ((0, 1), (0, 0))).transpose((1, 0))
  
      _logepsilon = -100000.0
  
      def _iterate(prev, x):
          prev_mask, prev_emit = prev
          logprob_mask, logprob_emit, pad = x
  
          prev_mask_orig = prev_mask
          prev_mask = prev_mask.at[:, 1:].set(
              jnp.logaddexp(
                  prev_mask[:, 1:], prev_emit + _logepsilon * repeat
              ),
          )
          emit = jnp.logaddexp(
              prev_mask[:, :-1] + logprob_emit, prev_emit + logprob_emit
          )
  
          mask = prev_mask + logprob_mask[:, None]
          mask = mask.at[:, 1:].set(
              jnp.logaddexp(
                  mask[:, 1:],
                  prev_emit
                  + logprob_mask[:, None]
                  + _logepsilon * (1 - repeat),
              )
          )
  
          pad = pad[:, None]
          emit = emit * pad + prev_emit * (1 - pad)
          mask = mask * pad + prev_mask_orig * (1 - pad)
  
          return (mask, emit), (mask, emit)
  
      mask_init = jnp.full(
          (batch_size, max_target_length + 1), _logepsilon
      )
      mask_init = mask_init.at[:, 0].set(0.0)
      emit_init = jnp.full((batch_size, max_target_length), _logepsilon)
  
      _, (alphas_mask, alphas_emit) = lax.scan(
          _iterate,
          (mask_init, emit_init),
          (logprobs_mask, logprobs_emit, logit_paddings.transpose()),
      )
  
      last_alpha_mask = (
          alphas_mask[-1]
          .at[:, 1:]
          .set(jnp.logaddexp(alphas_mask[-1, :, 1:], alphas_emit[-1]))
      )
  
      return -last_alpha_mask[jnp.arange(batch_size), target_length]
  
imports: import jax
  import jax.numpy
  import numpy
  from jax import lax
  from jax import nn
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_jax
  from keras.backend.config import epsilon
  from keras.backend.jax.core import cast
  from keras.backend.jax.core import convert_to_tensor
label_desc: Use image node_id 33 for calling a global function with example usage: ctc_loss(target, output, target_length, output_length, mask_index) and returns: unknown, mask,  emit,  mask,  emit