class_docstr: A Dataset Object, used to easily import edgelist data and cuGraph.Graph
  instances.
  
  Parameters
  ----------
  meta_data_file_name : yaml file
      The metadata file for the specific graph dataset, which includes
      information on the name, type, url link, data loading format, graph
      properties
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docstr: Return a distributed Graph object.
  
  Parameters
  ----------
  download : Boolean (default=False)
      Downloads the dataset from the web.
  
  create_using: cugraph.Graph (instance or class), optional
  (default=Graph)
      Specify the type of Graph to create. Can pass in an instance to
      create a Graph instance with specified 'directed' attribute.
  
  ignore_weights : Boolean (default=False)
      Ignores weights in the dataset if True, resulting in an
      unweighted Graph. If False (the default), weights from the
      dataset -if present- will be applied to the Graph. If the
      dataset does not contain weights, the Graph returned will
      be unweighted regardless of ignore_weights.
  
  store_transposed : bool, optional (default=False)
      If True, stores the transpose of the adjacency matrix.  Required
      for certain algorithms.
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code: def get_dask_graph(
      self,
      download=False,
      create_using=Graph,
      ignore_weights=False,
      store_transposed=False,
  ):
      """
      Return a distributed Graph object.
  
      Parameters
      ----------
      download : Boolean (default=False)
          Downloads the dataset from the web.
  
      create_using: cugraph.Graph (instance or class), optional
      (default=Graph)
          Specify the type of Graph to create. Can pass in an instance to
          create a Graph instance with specified 'directed' attribute.
  
      ignore_weights : Boolean (default=False)
          Ignores weights in the dataset if True, resulting in an
          unweighted Graph. If False (the default), weights from the
          dataset -if present- will be applied to the Graph. If the
          dataset does not contain weights, the Graph returned will
          be unweighted regardless of ignore_weights.
  
      store_transposed : bool, optional (default=False)
          If True, stores the transpose of the adjacency matrix.  Required
          for certain algorithms.
      """
      if self._edgelist is None:
          self.get_dask_edgelist(download)
  
      if create_using is None:
          G = Graph()
      elif isinstance(create_using, Graph):
          attrs = {"directed": create_using.is_directed()}
          G = type(create_using)(**attrs)
      elif issubclass(create_using, Graph):
          G = create_using()
      else:
          raise TypeError(
              "create_using must be a cugraph.Graph "
              "(or subclass) type or instance, got: "
              f"{type(create_using)}"
          )
  
      if len(self.metadata["col_names"]) > 2 and not (ignore_weights):
          G.from_dask_cudf_edgelist(
              self._edgelist,
              source=self.metadata["col_names"][0],
              destination=self.metadata["col_names"][1],
              edge_attr=self.metadata["col_names"][2],
              store_transposed=store_transposed,
          )
      else:
          G.from_dask_cudf_edgelist(
              self._edgelist,
              source=self.metadata["col_names"][0],
              destination=self.metadata["col_names"][1],
              store_transposed=store_transposed,
          )
      return G
  
imports: import cudf
  import dask_cudf
  import yaml
  import os
  import pandas
  import cugraph.dask
  from pathlib import Path
  import urllib.request
  from cugraph.structure.graph_classes import Graph
label_desc: Use image node_id 8 for calling the Dataset obj's underlying member method code with example usage: obj.get_dask_graph(download, create_using, ignore_weights, store_transposed) and returns: G