code: def _make_fake_dataset_fn(initial_delay_us, remainder_delay_us):
      """Returns a dataset that emulates a remote storage data source.
  
      Returns a dataset factory which creates a dataset with 100 elements that
      emulates the performance characteristic of a file-based dataset stored in a
      remote storage. In particular, the first element will take an order of
      magnitude longer to produce than the remaining elements (100ms vs. 1ms).
  
      Args:
        initial_delay_us: How long to wait before producing the first element.
        remainder_delay_us: How long to wait before producing subsequent elements.
      """
  
      def fake_dataset_fn(unused):
          """Returns a function that creates a dataset with the specified delays."""
          del unused
  
          def make_dataset(time_us, num_elements):
              dataset = dataset_ops.Dataset.range(num_elements)
              if time_us > 0:
                  dataset = dataset.apply(testing.sleep(time_us))
              return dataset
  
          if not initial_delay_us:
              return make_dataset(remainder_delay_us, 100)
  
          return make_dataset(initial_delay_us, 0).concatenate(
              make_dataset(remainder_delay_us, 100)
          )
  
      return fake_dataset_fn
  
file_path: tensorflow-upstream/tensorflow/python/data/benchmarks/interleave_benchmark.py
imports: from tensorflow.python.data.benchmarks import benchmark_base
  from tensorflow.python.data.experimental.ops import interleave_ops
  from tensorflow.python.data.experimental.ops import testing
  from tensorflow.python.data.ops import dataset_ops
label_desc: Use image node_id 1 for calling a global function with example usage: _make_fake_dataset_fn(initial_delay_us, remainder_delay_us) and returns: fake_dataset_fn, make_dataset, dataset, make_dataset