code: def test_fit_chain(X):
      d = Normal(covariance_type="full").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(covariance_type="full").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 34 for calling a global function with example usage: test_fit_chain(X) without return types