code: def test_wrong_inputs():
      with pytest.raises(
          ValueError,
          match=r"Argument num_features must be greater to zero, got:",
      ):
          InceptionScore(
              num_features=-1, feature_extractor=torch.nn.Identity()
          ).update(torch.rand(2, 0))
  
      with pytest.raises(
          ValueError,
          match=r"feature_extractor output must be a tensor of dim 2, got: 1",
      ):
          InceptionScore(
              num_features=1000, feature_extractor=torch.nn.Identity()
          ).update(torch.rand(3))
  
      with pytest.raises(
          ValueError,
          match=r"Batch size should be greater than one, got: 0",
      ):
          InceptionScore(
              num_features=1000, feature_extractor=torch.nn.Identity()
          ).update(torch.rand(0, 0))
  
      with pytest.raises(
          ValueError,
          match=r"num_features returned by feature_extractor should be 1000, got: 0",
      ):
          InceptionScore(
              num_features=1000, feature_extractor=torch.nn.Identity()
          ).update(torch.rand(2, 0))
  
      with pytest.raises(
          NotComputableError,
          match=r"InceptionScore must have at least one example before it can be computed.",
      ):
          InceptionScore(
              num_features=1000, feature_extractor=torch.nn.Identity()
          ).compute()
  
      with pytest.raises(
          ValueError,
          match=r"Argument num_features must be provided, if feature_extractor is specified.",
      ):
          InceptionScore(feature_extractor=torch.nn.Identity())
  
file_path: ignite/tests/ignite/metrics/gan/test_inception_score.py
imports: import os
  import pytest
  import torch
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics.gan.inception_score import InceptionScore
label_desc: Use image node_id 4 for calling a global function with example usage: test_wrong_inputs() without return types