code: def mnist_fake_dataset():
      imgs = []
      labels = []
      for i in range(_STEPS * _BATCH_SIZE):
          img = stateless_random_ops.stateless_random_uniform(
              shape=(28, 28, 1),
              seed=[1, i],
              minval=0,
              maxval=256,
              dtype=dtypes.float32,
          )
          imgs.append(img)
          label = stateless_random_ops.stateless_random_uniform(
              shape=(1,),
              seed=[2, i],
              minval=0,
              maxval=10,
              dtype=dtypes.int64,
          )
          labels.append(label)
  
      return dataset_ops.DatasetV2.from_tensor_slices(
          (array_ops_stack.stack(imgs), array_ops_stack.stack(labels))
      )
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import d_variable
  from tensorflow.dtensor.python import input_util
  from tensorflow.dtensor.python import layout
  from tensorflow.dtensor.python.tests import test_util
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager.polymorphic_function import polymorphic_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling a global function with example usage: mnist_fake_dataset() and returns: dataset_ops