code: def read_csv(input_csv_file, scale):
      """
      Returns a dask_cudf DataFrame from reading input_csv_file.
  
      All input CSV files should be weighted with random values of a uniform
      distribution from the interval [0, 1) in order to best simulate the output
      of a Graph500-compliant graph generator.
  
      scale is used to determine the data type for the vertex ID values in the
      DataFrame. (num verts = 2^scale), which is used to determine
      """
      vertex_t = "int32" if scale <= 32 else "int64"
      dtypes = [vertex_t, vertex_t, "float32"]
      names = (["src", "dst", "weight"],)
  
      chunksize = cugraph.dask.get_chunksize(input_csv_file)
      return dask_cudf.read_csv(
          input_csv_file,
          chunksize=chunksize,
          delimiter=" ",
          # names=names,
          dtype=dtypes,
          header=None,
      )
  
imports: import numpy
  import dask_cudf
  from dask.distributed import Client
  from dask_cuda import LocalCUDACluster
  from cugraph.structure.symmetrize import symmetrize_ddf
  from cugraph.dask.common.mg_utils import get_visible_devices
  from dask_cuda.initialize import initialize
  import cudf
  import cugraph
  from cugraph.dask.comms import comms
  import tempfile
  from cugraph.testing.mg_utils import generate_edgelist
label_desc: Use image node_id 1 for calling a global function with example usage: read_csv(input_csv_file, scale) and returns: dask_cudf