class_docstr: A dataset that loads previously saved dataset.
class_docstr_tok: ["A","dataset","that","loads","previously","saved","dataset","."]
code: def __init__(self, path, element_spec, compression, reader_func):
      self._path = path
      self._element_spec = element_spec
      self._compression = compression
      self._reader_func = structured_function.StructuredFunctionWrapper(
          reader_func,
          "load()",
          # Dataset of datasets of input elements
          input_structure=dataset_ops.DatasetSpec(
              dataset_ops.DatasetSpec(self._element_spec)
          ),
      )
  
      variant_tensor = ged_ops.load_dataset(
          path,
          reader_func_other_args=self._reader_func.function.captured_inputs,
          compression=compression,
          reader_func=self._reader_func.function,
          **self._flat_structure,
      )
      super().__init__(variant_tensor)
  
imports: import multiprocessing
  import os
  import time
  from typing import Optional
  from google.protobuf import message
  from google.protobuf import text_format
  from tensorflow.core.protobuf import snapshot_pb2
  from tensorflow.python.data.experimental.service import _pywrap_snapshot_utils
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import structured_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.ops import gen_experimental_dataset_ops
  from tensorflow.python.platform import gfile
  from tensorflow.python.saved_model import nested_structure_coder
label_desc: Use image node_id 1 to create a new _LoadDataset object from inherited base classes: dataset_ops with example: obj = _LoadDataset(path, element_spec, compression, reader_func)