code: def test_isomap_fit_precomputed_radius_graph(global_dtype):
      # Isomap.fit_transform must yield similar result when using
      # a precomputed distance matrix.
  
      X, y = datasets.make_s_curve(200, random_state=0)
      X = X.astype(global_dtype, copy=False)
      radius = 10
  
      g = neighbors.radius_neighbors_graph(
          X, radius=radius, mode="distance"
      )
      isomap = manifold.Isomap(
          n_neighbors=None, radius=radius, metric="precomputed"
      )
      isomap.fit(g)
      precomputed_result = isomap.embedding_
  
      isomap = manifold.Isomap(
          n_neighbors=None, radius=radius, metric="minkowski"
      )
      result = isomap.fit_transform(X)
      atol = 1e-5 if global_dtype == np.float32 else 0
      assert_allclose(precomputed_result, result, atol=atol)
  
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 10 for calling a global function with example usage: test_isomap_fit_precomputed_radius_graph(global_dtype) without return types