code: def _forwardAndBackward(self, sparse_labels, logits_time_major, seed):
      np.random.seed(seed)
      params = self._genInputParams(
          logits_time_major=logits_time_major,
          sparse_labels=sparse_labels,
      )
      labels, logits, labels_lengths, logits_lengths = params
      output_shape = (labels_lengths.shape[0],)
      upstream_gradients = self._randomFloats(output_shape)
      with backprop.GradientTape() as tape:
          tape.watch(logits)
          loss = ctc_ops.ctc_loss_v3(
              labels,
              logits,
              labels_lengths,
              logits_lengths,
              logits_time_major=logits_time_major,
              blank_index=0,
          )
          gradient_injector_output = loss * upstream_gradients
      return loss, tape.gradient(gradient_injector_output, logits)
  
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 3 for calling the CTCLossDeterministicTest obj's underlying member method code with example usage: obj._forwardAndBackward(sparse_labels, logits_time_major, seed) and returns: loss,  tape