code: def test_transform(global_dtype, n_neighbors, radius):
      n_samples = 200
      n_components = 10
      noise_scale = 0.01
  
      # Create S-curve dataset
      X, y = datasets.make_s_curve(n_samples, random_state=0)
  
      X = X.astype(global_dtype, copy=False)
  
      # Compute isomap embedding
      iso = manifold.Isomap(
          n_components=n_components,
          n_neighbors=n_neighbors,
          radius=radius,
      )
      X_iso = iso.fit_transform(X)
  
      # Re-embed a noisy version of the points
      rng = np.random.RandomState(0)
      noise = noise_scale * rng.randn(*X.shape)
      X_iso2 = iso.transform(X + noise)
  
      # Make sure the rms error on re-embedding is comparable to noise_scale
      assert np.sqrt(np.mean((X_iso - X_iso2) ** 2)) < 2 * noise_scale
  
imports: import math
  from itertools import product
  import numpy
  import pytest
  from scipy.sparse import rand
  from sklearn import clone, datasets, manifold, neighbors, pipeline, preprocessing
  from sklearn.datasets import make_blobs
  from sklearn.metrics.pairwise import pairwise_distances
  from sklearn.utils._testing import assert_allclose, assert_allclose_dense_sparse, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 4 for calling a global function with example usage: test_transform(global_dtype, n_neighbors, radius) without return types