code: def test_predict_proba_one_edge_raises():
      m = Categorical([[1.0 / 3, 1.0 / 3, 1.0 / 3]])
      f = Categorical([[0.23, 0.17, 0.60]])
      model = FactorGraph([f], [m], [(m, f)])
  
      X = torch.tensor([[0, 0]])
      mask = torch.tensor([[False, True]])
      X_masked = torch.masked.MaskedTensor(X, mask=mask)
      assert_raises(ValueError, model.predict_proba, X_masked)
  
      X = torch.tensor([[3]])
      mask = torch.tensor([[True]])
      X_masked = torch.masked.MaskedTensor(X, mask=mask)
      assert_raises(IndexError, model.predict_proba, X_masked)
  
      mask = torch.tensor([[False]])
      X_masked = torch.masked.MaskedTensor(X, mask=mask)
      model.predict_proba(X_masked)
  
imports: import numpy
  import torch
  import pytest
  from pomegranate.factor_graph import FactorGraph
  from pomegranate.distributions import Exponential
  from pomegranate.distributions import Categorical
  from pomegranate.distributions import JointCategorical
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 18 for calling a global function with example usage: test_predict_proba_one_edge_raises() without return types