class_docstr: Inception Model pre-trained on the ImageNet Dataset.
  
  Args:
      return_features: set it to `True` if you want the model to return features from the last pooling
          layer instead of prediction probabilities.
      device: specifies which device updates are accumulated on. Setting the
          metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
          non-blocking. By default, CPU.
class_docstr_tok: ["Inception","Model","pre-trained","on","the","ImageNet","Dataset",".","Args",":","return_features",":","set","it","to","`","True","`","if","you","want","the","model","to","return","features","from","the","last","pooling","layer","instead","of","prediction","probabilities",".","device",":","specifies","which","device","updates","are","accumulated","on",".","Setting","the","metric","'s","device","to","be","the","same","as","your","``","update","``","arguments","ensures","the","``","update","``","method","is","non-blocking",".","By","default",",","CPU","."]
code: def forward(self, data: torch.Tensor) -> torch.Tensor:
      if data.dim() != 4:
          raise ValueError(
              f"Inputs should be a tensor of dim 4, got {data.dim()}"
          )
      if data.shape[1] != 3:
          raise ValueError(
              f"Inputs should be a tensor with 3 channels, got {data.shape}"
          )
      if data.device != torch.device(self._device):
          data = data.to(self._device)
      return self.model(data)
  
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 InceptionModel obj's underlying member method code with example usage: obj.forward(data) and returns: self