code: def forward(self, x: Tensor) -> InceptionOutputs:
      x = self._transform_input(x)
      x, aux = self._forward(x)
      aux_defined = self.training and self.aux_logits
      if torch.jit.is_scripting():
          if not aux_defined:
              warnings.warn(
                  "Scripted Inception3 always returns Inception3 Tuple"
              )
          return InceptionOutputs(x, aux)
      else:
          return self.eager_outputs(x, aux)
  
imports: import warnings
  from collections import namedtuple
  from functools import partial
  from typing import Any, Callable, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..transforms._presets import ImageClassification
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 5 for calling the Inception3 obj's underlying member method code with example usage: obj.forward(x) and returns: InceptionOutputs, self