code: def random_edgelist(
      e=1024,
      ef=16,
      dtypes={"src": np.int32, "dst": np.int32, "val": float},
      drop_duplicates=True,
      seed=None,
  ):
      """Create a random edge list
  
      Parameters
      ----------
      e : int
          Number of edges
      ef : int
          Edge factor (average number of edges per vertex)
      dtypes : dict
          Mapping of column names to types.
          Supported type is {"src": int, "dst": int, "val": float}
      drop_duplicates
          Drop duplicates
      seed : int (optional)
          Randomstate seed
  
      Examples
      --------
      >>> from cugraph.testing import utils
      >>> # genrates 20 df with 100M edges each and write to disk
      >>> for x in range(20):
      >>>    df = utils.random_edgelist(e=100000000, ef=64,
      >>>                               dtypes={'src':np.int32, 'dst':np.int32},
      >>>                               seed=x)
      >>>    df.to_csv('df'+str(x), header=False, index=False)
      >>>    #df.to_parquet('files_parquet/df'+str(x), index=False)
      """
      state = np.random.RandomState(seed)
      columns = dict(
          (k, make[dt](e // ef, e, state)) for k, dt in dtypes.items()
      )
  
      df = pd.DataFrame(columns)
      if drop_duplicates:
          df = df.drop_duplicates(subset=["src", "dst"])
          print("Generated " + str(df.shape[0]) + " edges")
      return df
  
imports: import os
  import pytest
  import pandas
  import networkx
  import numpy
  import cupy
  from cupyx.scipy.sparse import coo_matrix
  from cupyx.scipy.sparse import csr_matrix
  from cupyx.scipy.sparse import csc_matrix
  from scipy.sparse import coo_matrix
  from scipy.sparse import csr_matrix
  from scipy.sparse import csc_matrix
  from pathlib import Path
  import cudf
  import dask_cudf
  import cugraph
  from cugraph.dask.common.mg_utils import get_client
label_desc: Use image node_id 10 for calling a global function with example usage: random_edgelist(e, ef, dtypes, drop_duplicates, seed) and returns: df