code: def test_random_projection_fit(datatype, method):
      # dataset generation
      data, target = make_blobs(
          n_samples=800, centers=400, n_features=3000
      )
  
      # conversion to input_type
      data = data.astype(datatype)
      target = target.astype(datatype)
  
      # creation of model
      if method == "gaussian":
          model = GaussianRandomProjection(eps=0.2)
      else:
          model = SparseRandomProjection(eps=0.2)
  
      # fitting the model
      model.fit(data)
  
      assert True
  
file_path: cuml/python/cuml/tests/test_random_projection.py
imports: from cuml.common import has_scipy
  from sklearn.datasets import make_blobs
  from sklearn.random_projection import johnson_lindenstrauss_min_dim
  from cuml.random_projection import johnson_lindenstrauss_min_dim
  from cuml.random_projection import GaussianRandomProjection, SparseRandomProjection
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: test_random_projection_fit(datatype, method) without return types