class_docstr: Module that never leaves evaluation mode.
class_docstr_tok: ["Module","that","never","leaves","evaluation","mode","."]
code: def __init__(
      self,
      name: str,
      features_list: List[str],
      feature_extractor_weights_path: Optional[str] = None,
  ) -> None:
      if not _TORCH_FIDELITY_AVAILABLE:
          raise ModuleNotFoundError(
              "NoTrainInceptionV3 module requires that `Torch-fidelity` is installed."
              " Either install as `pip install torchmetrics[image]` or `pip install torch-fidelity`."
          )
  
      super().__init__(
          name, features_list, feature_extractor_weights_path
      )
      # put into evaluation mode
      self.eval()
  
imports: from copy import deepcopy
  from typing import Any, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torch.nn.functional import adaptive_avg_pool2d
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 1 to create a new NoTrainInceptionV3 object from inherited base classes: _FeatureExtractorInceptionV3 with example: obj = NoTrainInceptionV3(name, features_list, feature_extractor_weights_path)