code: def test_fit(X, means, covs):
      d = StudentT(3, means, covs)
      d.fit(X)
      assert_raises(
          AssertionError, assert_array_almost_equal, means, d.means
      )
      assert_array_almost_equal(
          d.covs, torch.diag(torch.cov(torch.tensor(X).T, correction=0))
      )
  
      for param1, param2 in (means, covs), (None, None):
          d = StudentT(3, param1, param2)
          d.fit(X[:4])
          _test_fit_params(
              d, [1.525, 1.375, 1.55], [0.931875, 0.626875, 0.5025]
          )
  
          d.fit(X[4:])
          _test_fit_params(
              d, [3.666667, 1.4, 1.466667], [1.742223, 0.14, 3.215556]
          )
  
          d = StudentT(3, param1, param2)
          d.fit(X)
          _test_fit_params(
              d,
              [2.442857, 1.385714, 1.514286],
              [2.402449, 0.418367, 1.666939],
          )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import StudentT
  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(X, means, covs) without return types