code: def load(path, element_spec=None, compression=None, reader_func=None):
      """Loads a previously saved dataset.
  
      Example usage:
  
      >>> import tempfile
      >>> path = os.path.join(tempfile.gettempdir(), "saved_data")
      >>> # Save a dataset
      >>> dataset = tf.data.Dataset.range(2)
      >>> tf.data.experimental.save(dataset, path)
      >>> new_dataset = tf.data.experimental.load(path)
      >>> for elem in new_dataset:
      ...   print(elem)
      tf.Tensor(0, shape=(), dtype=int64)
      tf.Tensor(1, shape=(), dtype=int64)
  
  
      If the default option of sharding the saved dataset was used, the element
      order of the saved dataset will be preserved when loading it.
  
      The `reader_func` argument can be used to specify a custom order in which
      elements should be loaded from the individual shards. The `reader_func` is
      expected to take a single argument -- a dataset of datasets, each containing
      elements of one of the shards -- and return a dataset of elements. For
      example, the order of shards can be shuffled when loading them as follows:
  
      ```python
      def custom_reader_func(datasets):
        datasets = datasets.shuffle(NUM_SHARDS)
        return datasets.interleave(lambda x: x, num_parallel_calls=AUTOTUNE)
  
      dataset = tf.data.experimental.load(
          path="/path/to/data", ..., reader_func=custom_reader_func)
      ```
  
      Args:
        path: Required. A path pointing to a previously saved dataset.
        element_spec: Optional. A nested structure of `tf.TypeSpec` objects matching
          the structure of an element of the saved dataset and specifying the type
          of individual element components. If not provided, the nested structure of
          `tf.TypeSpec` saved with the saved dataset is used. Note that this
          argument is required in graph mode.
        compression: Optional. The algorithm to use to decompress the data when
          reading it. Supported options are `GZIP` and `NONE`. Defaults to `NONE`.
        reader_func: Optional. A function to control how to read data from shards.
          If present, the function will be traced and executed as graph computation.
  
      Returns:
        A `tf.data.Dataset` instance.
  
      Raises:
        FileNotFoundError: If `element_spec` is not specified and the saved nested
          structure of `tf.TypeSpec` can not be located with the saved dataset.
        ValueError: If `element_spec` is not specified and the method is executed
          in graph mode.
      """
      return dataset_ops.Dataset.load(
          path, element_spec, compression, reader_func
      )
  
imports: from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling a global function with example usage: load(path, element_spec, compression, reader_func) and returns: dataset_ops