code: def test_euclidean_distances():
      X = da.random.uniform(size=(100, 4), chunks=50)
      Y = da.random.uniform(size=(100, 4), chunks=50)
      a = dask_ml.metrics.euclidean_distances(X, Y)
      b = sklearn.metrics.euclidean_distances(X, Y)
      assert_eq(a, b)
  
      x_norm_squared = (X**2).sum(axis=1).compute()[:, np.newaxis]
      a = dask_ml.metrics.euclidean_distances(
          X, Y, X_norm_squared=x_norm_squared
      )
      b = sklearn.metrics.euclidean_distances(
          X, Y, X_norm_squared=x_norm_squared
      )
      assert_eq(a, b)
  
      y_norm_squared = (Y**2).sum(axis=1).compute()[np.newaxis, :]
      a = dask_ml.metrics.euclidean_distances(
          X, Y, Y_norm_squared=y_norm_squared
      )
      b = sklearn.metrics.euclidean_distances(
          X, Y, Y_norm_squared=y_norm_squared
      )
      assert_eq(a, b)
  
file_path: dask-ml/tests/metrics/test_metrics.py
imports: import dask
  import dask.array
  import numpy
  import numpy.testing
  import pytest
  import sklearn
  import sklearn.linear_model
  import sklearn.metrics
  from dask.array.utils import assert_eq
  import dask_ml.metrics
  import dask_ml.wrappers
label_desc: Use image node_id 3 for calling a global function with example usage: test_euclidean_distances() without return types