code: def test_fit(X, means, covs):
      d = Normal(means, covs, covariance_type="full")
      d.fit(X)
      assert_raises(
          AssertionError, assert_array_almost_equal, means, d.means
      )
      assert_array_almost_equal(
          d.covs, torch.cov(torch.tensor(X).T, correction=0)
      )
  
      for param1, param2 in (means, covs), (None, None):
          d = Normal(param1, param2, covariance_type="full")
          d.fit(X[:4])
          _test_fit_params(
              d,
              [1.525, 1.375, 1.55],
              [
                  [0.931875, 0.518125, 0.41125],
                  [0.518125, 0.626875, -0.04375],
                  [0.41125, -0.04375, 0.5025],
              ],
          )
  
          d.fit(X[4:])
          _test_fit_params(
              d,
              [3.666667, 1.4, 1.466667],
              [
                  [1.742223, 0.373333, 2.235555],
                  [0.373333, 0.14, 0.623333],
                  [2.235555, 0.623333, 3.215556],
              ],
          )
  
          d = Normal(param1, param2, covariance_type="full")
          d.fit(X)
          _test_fit_params(
              d,
              [2.442857, 1.385714, 1.514286],
              [
                  [2.40245, 0.469184, 1.149389],
                  [0.469184, 0.418367, 0.241633],
                  [1.149389, 0.241633, 1.666939],
              ],
          )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Normal
  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 32 for calling a global function with example usage: test_fit(X, means, covs) without return types