code: def test_log_probability(X, lambdas):
      p = [1.7]
      x = [[1.0], [2.0], [8.0], [3.0], [2.0]]
      y = [-1.169372, -1.331891, -8.059577, -1.899875, -1.331891]
  
      x_torch = torch.tensor(numpy.array(x))
      p_torch = torch.tensor(numpy.array(p))
  
      d1 = Poisson(p)
      d2 = Poisson(numpy.array(p, dtype=numpy.float64))
      d3 = torch.distributions.Poisson(p_torch)
  
      _test_predictions(x, y, d1.log_probability(x), torch.float32)
      _test_predictions(x, y, d2.log_probability(x), torch.float64)
      _test_predictions(
          x,
          d3.log_prob(x_torch).sum(axis=1),
          d2.log_probability(x),
          torch.float64,
      )
  
      p = [1.7, 2.3, 0.9, 1.7, 4.1]
      x = [[1.0, 2.0, 8.0, 3.0, 2.0]]
      y = [-18.715237]
  
      p_torch = torch.tensor(numpy.array(p))
      x_torch = torch.tensor(numpy.array(x))
  
      d1 = Poisson(p)
      d2 = Poisson(numpy.array(p, dtype=numpy.float64))
      d3 = torch.distributions.Poisson(p_torch)
  
      _test_predictions(x, y, d1.log_probability(x), torch.float32)
      _test_predictions(x, y, d2.log_probability(x), torch.float64)
      _test_predictions(
          x,
          d3.log_prob(x_torch).sum(axis=1),
          d2.log_probability(x),
          torch.float64,
      )
  
      p = [1, 2, 4]
      x = [[1, 2, 1], [2, 2, 1], [0, 1, 0], [0, 0, 2]]
      y = [-4.920558, -5.613706, -6.306853, -4.920558]
  
      p_torch = torch.tensor(numpy.array(p))
      x_torch = torch.tensor(numpy.array(x))
  
      d1 = Poisson(p)
      d2 = Poisson(numpy.array(p, dtype=numpy.float64))
      d3 = torch.distributions.Poisson(p_torch)
  
      _test_predictions(x, y, d1.log_probability(x), torch.float32)
      _test_predictions(x, y, d2.log_probability(x), torch.float64)
      _test_predictions(
          x,
          d3.log_prob(x_torch).sum(axis=1),
          d2.log_probability(x),
          torch.float64,
      )
  
      p = [1.0, 2.0, 4.0]
      p_torch = torch.tensor(numpy.array(p))
  
      d1 = Poisson(p)
      d2 = Poisson(numpy.array(p, dtype=numpy.float64))
      d3 = torch.distributions.Poisson(p_torch)
  
      _test_predictions(x, y, d1.log_probability(x), torch.float32)
      _test_predictions(x, y, d2.log_probability(x), torch.float64)
      _test_predictions(
          x,
          d3.log_prob(x_torch).sum(axis=1),
          d2.log_probability(x),
          torch.float64,
      )
  
      p_torch = torch.tensor(numpy.array(lambdas))
      x_torch = torch.tensor(numpy.array(X))
  
      y = [
          -4.435252,
          -4.358063,
          -3.539164,
          -4.155351,
          -5.757008,
          -8.598402,
          -4.840717,
      ]
  
      d1 = Poisson(lambdas)
      d2 = Poisson(numpy.array(lambdas, dtype=numpy.float64))
      d3 = torch.distributions.Poisson(p_torch)
  
      _test_predictions(X, y, d1.log_probability(X), torch.float32)
      _test_predictions(X, y, d2.log_probability(X), torch.float64)
      _test_predictions(
          X,
          d3.log_prob(x_torch).sum(axis=1),
          d2.log_probability(X),
          torch.float64,
      )
  
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 17 for calling a global function with example usage: test_log_probability(X, lambdas) without return types