class_docstr: A GPU Graph Object (Base class of other graph types)
  
  Parameters
  ----------
  m_graph : cuGraph.MultiGraph object or None (default=None)
      Initialize the graph from a cugraph.MultiGraph object
  directed : boolean, optional (default=False)
      Indicated is the graph is directed.
  
  Examples
  --------
  >>> # undirected graph
  >>> G = cugraph.Graph()
  >>> # directed graph
  >>> G = cugraph.Graph(directed=True)
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docstr: Initialize a graph from the adjacency list. It is an error to call this
  method on an initialized Graph object. The passed offset_col and
  index_col arguments wrap gdf_column objects that represent a graph
  using the adjacency list format.
  If value_col is None, an unweighted graph is created. If value_col is
  not None, a weighted graph is created.
  Undirected edges must be stored as directed edges in both directions.
  
  Parameters
  ----------
  offset_col : cudf.Series
      This cudf.Series wraps a gdf_column of size V + 1 (V: number of
      vertices).  The gdf column contains the offsets for the vertices in
      this graph.
      Offsets must be in the range [0, E] (E: number of edges)
  
  index_col : cudf.Series
      This cudf.Series wraps a gdf_column of size E (E: number of edges).
      The gdf column contains the destination index for each edge.
      Destination indices must be in the range [0, V)
      (V: number of vertices).
  
  value_col : cudf.Series, optional (default=None)
      This pointer can be ``None``.  If not, this cudf.Series wraps a
      gdf_column of size E (E: number of edges).  The gdf column contains
      the weight value for each edge.  The expected type of
      the gdf_column element is floating point number.
  
  renumber : bool, optional (default=True)
      Indicate whether or not to renumber the source and destination
      vertex IDs.
  
  store_transposed : bool, optional (default=False)
      If True, stores the transpose of the adjacency matrix.  Required
      for certain algorithms.
  
  Examples
  --------
  >>> gdf = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ',
  ...                     dtype=['int32', 'int32', 'float32'],
  ...                     header=None)
  >>> M = gdf.to_pandas()
  >>> M = scipy.sparse.coo_matrix((M['2'],(M['0'],M['1'])))
  >>> M = M.tocsr()
  >>> offsets = cudf.Series(M.indptr)
  >>> indices = cudf.Series(M.indices)
  >>> G = cugraph.Graph()
  >>> G.from_cudf_adjlist(offsets, indices, None)
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code: def from_cudf_adjlist(
      self,
      offset_col,
      index_col,
      value_col=None,
      renumber=True,
      store_transposed=False,
  ):
      """
      Initialize a graph from the adjacency list. It is an error to call this
      method on an initialized Graph object. The passed offset_col and
      index_col arguments wrap gdf_column objects that represent a graph
      using the adjacency list format.
      If value_col is None, an unweighted graph is created. If value_col is
      not None, a weighted graph is created.
      Undirected edges must be stored as directed edges in both directions.
  
      Parameters
      ----------
      offset_col : cudf.Series
          This cudf.Series wraps a gdf_column of size V + 1 (V: number of
          vertices).  The gdf column contains the offsets for the vertices in
          this graph.
          Offsets must be in the range [0, E] (E: number of edges)
  
      index_col : cudf.Series
          This cudf.Series wraps a gdf_column of size E (E: number of edges).
          The gdf column contains the destination index for each edge.
          Destination indices must be in the range [0, V)
          (V: number of vertices).
  
      value_col : cudf.Series, optional (default=None)
          This pointer can be ``None``.  If not, this cudf.Series wraps a
          gdf_column of size E (E: number of edges).  The gdf column contains
          the weight value for each edge.  The expected type of
          the gdf_column element is floating point number.
  
      renumber : bool, optional (default=True)
          Indicate whether or not to renumber the source and destination
          vertex IDs.
  
      store_transposed : bool, optional (default=False)
          If True, stores the transpose of the adjacency matrix.  Required
          for certain algorithms.
  
      Examples
      --------
      >>> gdf = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ',
      ...                     dtype=['int32', 'int32', 'float32'],
      ...                     header=None)
      >>> M = gdf.to_pandas()
      >>> M = scipy.sparse.coo_matrix((M['2'],(M['0'],M['1'])))
      >>> M = M.tocsr()
      >>> offsets = cudf.Series(M.indptr)
      >>> indices = cudf.Series(M.indices)
      >>> G = cugraph.Graph()
      >>> G.from_cudf_adjlist(offsets, indices, None)
  
      """
      if self._Impl is None:
          self._Impl = simpleGraphImpl(self.graph_properties)
      elif type(self._Impl) is not simpleGraphImpl:
          raise RuntimeError("Graph is already initialized")
      elif (
          self._Impl.edgelist is not None
          or self._Impl.adjlist is not None
      ):
          raise RuntimeError("Graph already has values")
      self._Impl._simpleGraphImpl__from_adjlist(
          offset_col, index_col, value_col
      )
  
imports: import numpy
  from .graph_implementation import simpleGraphImpl, simpleDistributedGraphImpl, npartiteGraphImpl
  import cudf
  import dask_cudf
  from cugraph.utilities.utils import import_optional
label_desc: Use image node_id 5 for calling the Graph obj's underlying member method code with example usage: obj.from_cudf_adjlist(offset_col, index_col, value_col, renumber, store_transposed) without return types