code: def test_classifier_prediction_independent_of_X(
      strategy, global_random_seed
  ):
      y = [0, 2, 1, 1]
      X1 = [[0]] * 4
      clf1 = DummyClassifier(
          strategy=strategy, random_state=global_random_seed, constant=0
      )
      clf1.fit(X1, y)
      predictions1 = clf1.predict(X1)
  
      X2 = [[1]] * 4
      clf2 = DummyClassifier(
          strategy=strategy, random_state=global_random_seed, constant=0
      )
      clf2.fit(X2, y)
      predictions2 = clf2.predict(X2)
  
      assert_array_equal(predictions1, predictions2)
  
imports: import numpy
  import pytest
  import scipy.sparse
  from sklearn.base import clone
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.fixes import CSC_CONTAINERS
  from sklearn.utils.stats import _weighted_percentile
label_desc: Use image node_id 14 for calling a global function with example usage: test_classifier_prediction_independent_of_X(strategy, global_random_seed) without return types