code: def test_fit_weighted(X, w, mins, maxs):
      d = Uniform(mins, maxs)
      d.fit(X[:4], sample_weight=w[:4])
      _test_fit_params(d, [0.5, 0.2, 0.5], [3.1, 2.1, 2.2])
  
      d.fit(X[4:], sample_weight=w[4:])
      _test_fit_params(d, [2.2, 1.0, 0.1], [5.4, 1.9, 4.0])
  
      d = Uniform(mins, maxs)
      d.fit(X, sample_weight=w)
      _test_fit_params(d, [0.5, 0.2, 0.1], [5.4, 2.1, 4.0])
  
      X = [[1.2, 0.5, 1.1, 1.9], [6.2, 1.1, 2.4, 1.1]]
  
      w = [[1.1], [3.5]]
  
      d = Uniform()
      d.fit(X, sample_weight=w)
      _test_fit_params(d, [1.2, 0.5, 1.1, 1.1], [6.2, 1.1, 2.4, 1.9])
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Uniform
  from ._utils import _test_initialization_raises_two_parameters
  from ._utils import _test_initialization
  from ._utils import _test_predictions
  from ._utils import _test_efd_from_summaries
  from ._utils import _test_raises
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 35 for calling a global function with example usage: test_fit_weighted(X, w, mins, maxs) without return types