code: def build_dataset(
      self,
      multiplier=15.0,
      tensor_slice_len=2,
      batch_size=2,
      options=None,
  ):
      components = (
          np.arange(tensor_slice_len),
          np.array([[1, 2, 3]])
          * np.arange(tensor_slice_len)[:, np.newaxis],
          np.array(multiplier) * np.arange(tensor_slice_len),
      )
  
      dataset = (
          dataset_ops.Dataset.from_tensor_slices(components)
          .batch(batch_size)
          .unbatch()
      )
      if options:
          dataset = dataset.with_options(options)
      return dataset
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.python.data.kernel_tests import checkpoint_test_base
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import options
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import string_ops
  from tensorflow.python.ops.ragged import ragged_factory_ops
  from tensorflow.python.platform import test
  from tensorflow.python.util import compat
label_desc: Use image node_id 1 for calling the UnbatchCheckpointTest obj's underlying member method code with example usage: obj.build_dataset(multiplier, tensor_slice_len, batch_size, options) and returns: dataset