code: def test_summarize_dtypes(X, w, probs):
      X = numpy.array(X)
      probs = [numpy.array(prob, dtype=numpy.float32) for prob in probs]
  
      X = X.astype(numpy.int32)
      d = ConditionalCategorical(probs)
      assert d._xw_sum.dtype == torch.float32
      d.summarize(X)
      assert d._xw_sum.dtype == torch.float32
  
      X = X.astype(numpy.int64)
      d = ConditionalCategorical(probs)
      assert d._xw_sum.dtype == torch.float32
      d.summarize(X)
      assert d._xw_sum.dtype == torch.float32
  
      X = X.astype(numpy.int32)
      probs = [numpy.array(prob, dtype=numpy.float64) for prob in probs]
  
      d = ConditionalCategorical(probs)
      assert d._xw_sum.dtype == torch.float64
      d.summarize(X)
      assert d._xw_sum.dtype == torch.float64
  
      X = X.astype(numpy.int64)
      d = ConditionalCategorical(probs)
      assert d._xw_sum.dtype == torch.float64
      d.summarize(X)
      assert d._xw_sum.dtype == torch.float64
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.distributions import ConditionalCategorical
  from ._utils import _test_initialization_raises_one_parameter
  from ._utils import _test_initialization
  from ._utils import _test_predictions
  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 20 for calling a global function with example usage: test_summarize_dtypes(X, w, probs) without return types