code: def test_uniform_weights():
      # with uniform weights, results should be identical to stats.mode
      rng = np.random.RandomState(0)
      x = rng.randint(10, size=(10, 5))
      weights = np.ones(x.shape)
  
      for axis in (None, 0, 1):
          mode, score = _mode(x, axis)
          mode2, score2 = weighted_mode(x, weights, axis=axis)
  
          assert_array_equal(mode, mode2)
          assert_array_equal(score, score2)
  
imports: import numpy
  import pytest
  from scipy import linalg, sparse
  from scipy.linalg import eigh
  from scipy.sparse.linalg import eigsh
  from scipy.special import expit
  from sklearn.datasets import make_low_rank_matrix, make_sparse_spd_matrix
  from sklearn.utils import gen_batches
  from sklearn.utils._arpack import _init_arpack_v0
  from sklearn.utils._testing import assert_allclose, assert_allclose_dense_sparse, assert_almost_equal, assert_array_almost_equal, assert_array_equal, skip_if_32bit
  from sklearn.utils.extmath import _deterministic_vector_sign_flip, _incremental_mean_and_var, _randomized_eigsh, _safe_accumulator_op, cartesian, density, log_logistic, randomized_svd, row_norms, safe_sparse_dot, softmax, stable_cumsum, svd_flip, weighted_mode
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS, _mode
label_desc: Use image node_id 2 for calling a global function with example usage: test_uniform_weights() without return types