class_docstr: Test that SoftmaxCrossEntropyWithLogits operates reproducibly.
  
  Inheriting from xent_op_test_base.XentTestBase ensures that regular op
  functionality is correct when the deterministic code-path is selected.
  
  Note that because nn_ops.softmax_cross_entropy_with_logits calls
  nn_ops.cross_entropy_with_logits_v2, the focus of testing is on the
  former in order to test both.
code: def _generateInputs(
      self, dtype, seed=123, forward_not_backward=False
  ):
      batch_size = 1024
      if forward_not_backward and dtype == np.float16:
          # Generate more noise to expose the internal float32 implementation.
          # This is associated with significantly slower test cases (esp. on CPU).
          classes_count = 20000
      else:
          classes_count = 3000
      shape = (batch_size, classes_count)
      np.random.seed(seed)
      labels = self._randomFloats(shape, dtype, normalized_rows=True)
      logits = self._randomFloats(shape, dtype)
      return labels, logits
  
imports: import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import config
  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 test_util
  from tensorflow.python.kernel_tests.nn_ops import xent_op_test_base
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops.nn_grad import _SoftmaxCrossEntropyWithLogitsGrad
  from tensorflow.python.platform import test
label_desc: Use image node_id 2 for calling the XentOpDeterministicTest obj's underlying member method code with example usage: obj._generateInputs(dtype, seed, forward_not_backward) and returns: labels,  logits