code: def test_initialization_float(means, covs):
      funcs = (
          lambda x: x,
          tuple,
          lambda x: numpy.array(x, dtype=numpy.float32),
          lambda x: torch.tensor(x, dtype=torch.float32),
          lambda x: torch.nn.Parameter(
              torch.tensor(x), requires_grad=False
          ),
      )
  
      for func in funcs:
          means_, covs_ = func(means), func(covs)
  
          _test_initialization(
              Normal(
                  means_,
                  covs_,
                  covariance_type="full",
                  inertia=0.0,
                  frozen=False,
              ),
              means_,
              "means",
              0.0,
              False,
              torch.float32,
          )
          _test_initialization(
              Normal(
                  means_,
                  covs_,
                  covariance_type="full",
                  inertia=0.3,
                  frozen=False,
              ),
              means_,
              "means",
              0.3,
              False,
              torch.float32,
          )
          _test_initialization(
              Normal(
                  means_,
                  covs_,
                  covariance_type="full",
                  inertia=1.0,
                  frozen=True,
              ),
              means_,
              "means",
              1.0,
              True,
              torch.float32,
          )
          _test_initialization(
              Normal(
                  means_,
                  covs_,
                  covariance_type="full",
                  inertia=1.0,
                  frozen=False,
              ),
              means_,
              "means",
              1.0,
              False,
              torch.float32,
          )
  
          _test_initialization(
              Normal(
                  means_,
                  covs_,
                  covariance_type="full",
                  inertia=0.0,
                  frozen=False,
              ),
              covs_,
              "covs",
              0.0,
              False,
              torch.float32,
          )
          _test_initialization(
              Normal(
                  means_,
                  covs_,
                  covariance_type="full",
                  inertia=0.3,
                  frozen=False,
              ),
              covs_,
              "covs",
              0.3,
              False,
              torch.float32,
          )
          _test_initialization(
              Normal(
                  means_,
                  covs_,
                  covariance_type="full",
                  inertia=1.0,
                  frozen=True,
              ),
              covs_,
              "covs",
              1.0,
              True,
              torch.float32,
          )
          _test_initialization(
              Normal(
                  means_,
                  covs_,
                  covariance_type="full",
                  inertia=1.0,
                  frozen=False,
              ),
              covs_,
              "covs",
              1.0,
              False,
              torch.float32,
          )
  
      means_ = numpy.array(means, dtype=numpy.float64)
      covs_ = numpy.array(covs, dtype=numpy.float64)
  
      _test_initialization(
          Normal(
              means_,
              covs_,
              covariance_type="full",
              inertia=0.0,
              frozen=False,
          ),
          means_,
          "means",
          0.0,
          False,
          torch.float64,
      )
      _test_initialization(
          Normal(
              means_,
              covs_,
              covariance_type="full",
              inertia=0.3,
              frozen=False,
          ),
          means_,
          "means",
          0.3,
          False,
          torch.float64,
      )
      _test_initialization(
          Normal(
              means_,
              covs_,
              covariance_type="full",
              inertia=1.0,
              frozen=True,
          ),
          means_,
          "means",
          1.0,
          True,
          torch.float64,
      )
      _test_initialization(
          Normal(
              means_,
              covs_,
              covariance_type="full",
              inertia=1.0,
              frozen=False,
          ),
          means_,
          "means",
          1.0,
          False,
          torch.float64,
      )
  
      _test_initialization(
          Normal(
              means_,
              covs_,
              covariance_type="full",
              inertia=0.0,
              frozen=False,
          ),
          covs_,
          "covs",
          0.0,
          False,
          torch.float64,
      )
      _test_initialization(
          Normal(
              means_,
              covs_,
              covariance_type="full",
              inertia=0.3,
              frozen=False,
          ),
          covs_,
          "covs",
          0.3,
          False,
          torch.float64,
      )
      _test_initialization(
          Normal(
              means_,
              covs_,
              covariance_type="full",
              inertia=1.0,
              frozen=True,
          ),
          covs_,
          "covs",
          1.0,
          True,
          torch.float64,
      )
      _test_initialization(
          Normal(
              means_,
              covs_,
              covariance_type="full",
              inertia=1.0,
              frozen=False,
          ),
          covs_,
          "covs",
          1.0,
          False,
          torch.float64,
      )
  
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 6 for calling a global function with example usage: test_initialization_float(means, covs) without return types