code: def test_n_clusters(global_random_seed, global_dtype):
      # Test that n_clusters param works properly
      X, y = make_blobs(
          n_samples=100, centers=10, random_state=global_random_seed
      )
      X = X.astype(global_dtype, copy=False)
      brc1 = Birch(n_clusters=10)
      brc1.fit(X)
      assert len(brc1.subcluster_centers_) > 10
      assert len(np.unique(brc1.labels_)) == 10
  
      # Test that n_clusters = Agglomerative Clustering gives
      # the same results.
      gc = AgglomerativeClustering(n_clusters=10)
      brc2 = Birch(n_clusters=gc)
      brc2.fit(X)
      assert_array_equal(
          brc1.subcluster_labels_, brc2.subcluster_labels_
      )
      assert_array_equal(brc1.labels_, brc2.labels_)
  
      # Test that a small number of clusters raises a warning.
      brc4 = Birch(threshold=10000.0)
      with pytest.warns(ConvergenceWarning):
          brc4.fit(X)
  
imports: import numpy
  import pytest
  from sklearn.cluster import AgglomerativeClustering, Birch
  from sklearn.cluster.tests.common import generate_clustered_data
  from sklearn.datasets import make_blobs
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.metrics import pairwise_distances_argmin, v_measure_score
  from sklearn.utils._testing import assert_allclose, 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_n_clusters(global_random_seed, global_dtype) without return types