code: def read_csv_for_nx(
      csv_file, read_weights_in_sp=True, read_weights=True
  ):
      if read_weights:
          if read_weights_in_sp is True:
              df = pd.read_csv(
                  csv_file,
                  delimiter=" ",
                  header=None,
                  names=["0", "1", "weight"],
                  dtype={
                      "0": "int32",
                      "1": "int32",
                      "weight": "float32",
                  },
              )
          else:
              df = pd.read_csv(
                  csv_file,
                  delimiter=" ",
                  header=None,
                  names=["0", "1", "weight"],
                  dtype={
                      "0": "int32",
                      "1": "int32",
                      "weight": "float64",
                  },
              )
      else:
          df = pd.read_csv(
              csv_file,
              delimiter=" ",
              header=None,
              names=["0", "1"],
              usecols=["0", "1"],
              dtype={"0": "int32", "1": "int32"},
          )
      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 1 for calling a global function with example usage: read_csv_for_nx(csv_file, read_weights_in_sp, read_weights) and returns: df