code: def _check_feature_shapes(self, samples: torch.Tensor) -> None:
      if samples.dim() != 2:
          raise ValueError(
              f"feature_extractor output must be a tensor of dim 2, got: {samples.dim()}"
          )
  
      if samples.shape[0] == 0:
          raise ValueError(
              f"Batch size should be greater than one, got: {samples.shape[0]}"
          )
  
      if samples.shape[1] != self._num_features:
          raise ValueError(
              f"num_features returned by feature_extractor should be {self._num_features}, got: {samples.shape[1]}"
          )
  
file_path: ignite/ignite/metrics/gan/utils.py
imports: from typing import Callable, Optional, Union
  import torch
  from packaging.version import Version
  from ignite.metrics.metric import Metric
label_desc: Use image node_id 2 for calling the _BaseInceptionMetric obj's underlying member method code with example usage: obj._check_feature_shapes(samples) without return types