code: def _ctc_loss_impl(
      labels,
      inputs=None,
      sequence_length=None,
      preprocess_collapse_repeated=False,
      ctc_merge_repeated=True,
      ignore_longer_outputs_than_inputs=False,
      time_major=True,
      logits=None,
      use_cudnn=False,
  ):
      # Helper function of ctc_loss with one additional param:
      # use_cudnn: A bool to enable cuDNN CTC loss operation. If true, the blank
      #   index has to be 0.
  
      # The second, third, etc output tensors contain the gradients.  We use it in
      # _CTCLossGrad() below.
      if not isinstance(labels, sparse_tensor.SparseTensor):
          raise TypeError(
              "Expected argument `labels` to be a SparseTensor. "
              f"Received labels={labels} of type: "
              f"{type(labels).__name__}"
          )
  
      # For internal calculations, we transpose to [time, batch, num_classes]
      inputs = deprecation.deprecated_argument_lookup(
          "logits", logits, "inputs", inputs
      )
  
      inputs = ops.convert_to_tensor(inputs, name="logits")
      if not time_major:
          inputs = array_ops.transpose(
              inputs, [1, 0, 2]
          )  # (B,T,N) => (T,B,N)
  
      orig_dtype = inputs.dtype
      if orig_dtype in (dtypes.float16, dtypes.bfloat16):
          inputs = math_ops.cast(inputs, dtypes.float32)
  
      # gen_ctc_ops.ctc_loss_v2 differs from gen_ctc_ops.ctc_loss. v2 assumes the
      # blank index to be 0, but v1 views it as the last index.
      if use_cudnn:
          ctc_loss_func = gen_ctc_ops.ctc_loss_v2
      else:
          ctc_loss_func = gen_ctc_ops.ctc_loss
  
      loss, _ = ctc_loss_func(
          inputs,
          labels.indices,
          labels.values,
          sequence_length,
          preprocess_collapse_repeated=preprocess_collapse_repeated,
          ctc_merge_repeated=ctc_merge_repeated,
          ignore_longer_outputs_than_inputs=ignore_longer_outputs_than_inputs,
      )
  
      if orig_dtype in (dtypes.float16, dtypes.bfloat16):
          loss = math_ops.cast(loss, orig_dtype)
  
      return loss
  
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 4 for calling a global function with example usage: _ctc_loss_impl(labels, inputs, sequence_length, preprocess_collapse_repeated, ctc_merge_repeated, ignore_longer_outputs_than_inputs, time_major, logits, use_cudnn) and returns: loss