code: def test_masked_fit(X, X_masked):
      X = torch.tensor(numpy.array(X))
      mask = torch.ones_like(X).type(torch.bool)
      X_ = torch.masked.MaskedTensor(X, mask=mask)
  
      d = Poisson()
      d.fit(X_)
      _test_efd_from_summaries(
          d, "lambdas", "_log_lambdas", [2.0, 1.142857, 1.285714]
      )
  
      d = Poisson()
      d.fit(X_masked)
      _test_efd_from_summaries(
          d, "lambdas", "_log_lambdas", [2.25, 1.2, 1.5]
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.distributions import Poisson
  from ._utils import _test_initialization_raises_one_parameter
  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 44 for calling a global function with example usage: test_masked_fit(X, X_masked) without return types