code: def input_and_expected_output(request):
      """
      This fixture takes the above test data and converts it into everything
      needed to run a pylibcugraph CC algo for a specific input (either a
      adjacency matrix or a CSV edgelist file), and returns it along with the
      expected WCC and SCC result for each.
      """
      d = request.param.copy()
      input = d.pop("input")
      expected_output_dict = d
  
      if isinstance(input, Path):
          pdf = pd.read_csv(
              input,
              delimiter=" ",
              header=None,
              names=["0", "1", "weight"],
              dtype={"0": "int32", "1": "int32", "weight": "float32"},
          )
          num_verts = len(set(pdf["0"].tolist() + pdf["1"].tolist()))
          num_edges = len(pdf)
          weights = np.ones(num_edges)
          coo = coo_matrix(
              (weights, (pdf["0"], pdf["1"])),
              shape=(num_verts, num_verts),
              dtype=np.float32,
          )
          csr = coo.tocsr()
      else:
          csr = csr_matrix(input)
          num_verts = csr.get_shape()[0]
          num_edges = csr.nnz
  
      labels_to_populate = cp.zeros(num_verts, dtype=np.int32)
  
      return (
          (csr, labels_to_populate, num_verts, num_edges),
          expected_output_dict,
      )
  
file_path: cugraph/python/pylibcugraph/pylibcugraph/tests/test_connected_components.py
imports: from pathlib import Path
  import pytest
  import numpy
  import pandas
  import cupy
  from scipy.sparse import coo_matrix, csr_matrix
  from pylibcugraph import ResourceHandle, GraphProperties, SGGraph
  from pylibcugraph.testing import utils
label_desc: Use image node_id 1 for calling a global function with example usage: input_and_expected_output(request) and returns: csr,  labels_to_populate,  num_verts,  num_edges,  expected_output_dict