code: def testCtcLossDenseWithUndefinedStaticDimensions(
      self, unique, blank_index
  ):
      random_seed.set_random_seed(5)
  
      # Trace without a batch size and number of frames
      batch_size = None
      num_labels = 6
      label_length = 5
      num_frames = None
  
      @def_function.function
      def func(labels, logits, label_lengths, logit_lengths):
          unique_labels = (
              ctc_ops.ctc_unique_labels(labels) if unique else None
          )
          return ctc_ops.ctc_loss_dense(
              labels=labels,
              logits=logits,
              label_length=label_lengths,
              logit_length=logit_lengths,
              unique=unique_labels,
              blank_index=blank_index,
          )
  
      labels_spec = tensor_spec.TensorSpec(
          [batch_size, label_length], dtypes.int64
      )
      logits_spec = tensor_spec.TensorSpec(
          [num_frames, batch_size, num_labels], dtypes.float32
      )
      label_lengths_spec = tensor_spec.TensorSpec(
          [batch_size], dtypes.int64
      )
      logit_lengths_spec = tensor_spec.TensorSpec(
          [batch_size], dtypes.int64
      )
  
      f = func.get_concrete_function(
          labels_spec,
          logits_spec,
          label_lengths_spec,
          logit_lengths_spec,
      )
  
      # Execute with a defined batch size and number of frames
      batch_size = 8
      num_frames = 12
  
      logits = random_ops.random_uniform(
          [num_frames, batch_size, num_labels]
      )
      labels = random_ops.random_uniform(
          [batch_size, label_length],
          minval=1,
          maxval=num_labels,
          dtype=dtypes.int64,
      )
  
      label_lengths = random_ops.random_uniform(
          [batch_size],
          minval=2,
          maxval=label_length,
          dtype=dtypes.int64,
      )
      label_mask = array_ops.sequence_mask(
          label_lengths, maxlen=label_length, dtype=label_lengths.dtype
      )
      labels *= label_mask
  
      logit_lengths = constant_op.constant(
          [num_frames] * batch_size, dtype=dtypes.int64
      )
  
      f(labels, logits, label_lengths, logit_lengths)
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.python.eager import backprop
  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 dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import ctc_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 7 for calling the CTCLossTestV2 obj's underlying member method code with example usage: obj.testCtcLossDenseWithUndefinedStaticDimensions(unique, blank_index) and returns: ctc_ops