docstr: Initialize a graph from the edge list. It is an error to call this
  method on an initialized Graph object. The passed input_df argument
  wraps gdf_column objects that represent a graph using the edge list
  format. source argument is source column name and destination argument
  is destination column name.
  By default, renumbering is enabled to map the source and destination
  vertices into an index in the range [0, V) where V is the number
  of vertices.  If the input vertices are a single column of integers
  in the range [0, V), renumbering can be disabled and the original
  external vertex ids will be used.
  If weights are present, edge_attr argument is the weights column name.
  
  Parameters
  ----------
  input_df : cudf.DataFrame or dask_cudf.DataFrame
      A DataFrame that contains edge information. If a
      dask_cudf.DataFrame is passed it will be reinterpreted as a
      cudf.DataFrame. For the distributed path please use
      from_dask_cudf_edgelist.
  
  source : str or array-like, optional (default='source')
      Source column name or array of column names
  
  destination : str or array-like, optional (default='destination')
      Destination column name or array of column names
  
  edge_attr : str or None, optional (default=None)
      The weights column name
  
  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.
  
  legacy_renum_only : bool, optional (default=False)
      If True, skips the C++ renumbering step.  Must be true for
      pylibcugraph algorithms.  Must be false for algorithms
      not yet converted to the pylibcugraph C API.
  
      This parameter is deprecated and will be removed.
  
  Examples
  --------
  >>> df = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ',
  ...                    dtype=['int32', 'int32', 'float32'],
  ...                    header=None)
  >>> G = cugraph.BiPartiteGraph()
  >>> G.from_cudf_edgelist(df, source='0', destination='1',
  ...                      edge_attr='2', renumber=False)
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code: def from_cudf_edgelist(
      self,
      input_df,
      source="source",
      destination="destination",
      edge_attr=None,
      renumber=True,
      store_transposed=False,
      legacy_renum_only=False,
  ):
      """
      Initialize a graph from the edge list. It is an error to call this
      method on an initialized Graph object. The passed input_df argument
      wraps gdf_column objects that represent a graph using the edge list
      format. source argument is source column name and destination argument
      is destination column name.
      By default, renumbering is enabled to map the source and destination
      vertices into an index in the range [0, V) where V is the number
      of vertices.  If the input vertices are a single column of integers
      in the range [0, V), renumbering can be disabled and the original
      external vertex ids will be used.
      If weights are present, edge_attr argument is the weights column name.
  
      Parameters
      ----------
      input_df : cudf.DataFrame or dask_cudf.DataFrame
          A DataFrame that contains edge information. If a
          dask_cudf.DataFrame is passed it will be reinterpreted as a
          cudf.DataFrame. For the distributed path please use
          from_dask_cudf_edgelist.
  
      source : str or array-like, optional (default='source')
          Source column name or array of column names
  
      destination : str or array-like, optional (default='destination')
          Destination column name or array of column names
  
      edge_attr : str or None, optional (default=None)
          The weights column name
  
      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.
  
      legacy_renum_only : bool, optional (default=False)
          If True, skips the C++ renumbering step.  Must be true for
          pylibcugraph algorithms.  Must be false for algorithms
          not yet converted to the pylibcugraph C API.
  
          This parameter is deprecated and will be removed.
  
      Examples
      --------
      >>> df = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ',
      ...                    dtype=['int32', 'int32', 'float32'],
      ...                    header=None)
      >>> G = cugraph.BiPartiteGraph()
      >>> G.from_cudf_edgelist(df, source='0', destination='1',
      ...                      edge_attr='2', renumber=False)
  
      """
      if self._Impl is None:
          self._Impl = npartiteGraphImpl(self.graph_properties)
      # API may change in future
      self._Impl._npartiteGraphImpl__from_edgelist(
          input_df,
          source=source,
          destination=destination,
          edge_attr=edge_attr,
          renumber=renumber,
      )
  
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 2 for calling the NPartiteGraph obj's underlying member method code with example usage: obj.from_cudf_edgelist(input_df, source, destination, edge_attr, renumber, store_transposed, legacy_renum_only) without return types