code: def _transform_input(self, x: Tensor) -> Tensor:
      if self.transform_input:
          x_ch0 = (
              torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5)
              + (0.485 - 0.5) / 0.5
          )
          x_ch1 = (
              torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5)
              + (0.456 - 0.5) / 0.5
          )
          x_ch2 = (
              torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5)
              + (0.406 - 0.5) / 0.5
          )
          x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
      return x
  
imports: import warnings
  from collections import namedtuple
  from functools import partial
  from typing import Any, Callable, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import 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 2 for calling the GoogLeNet obj's underlying member method code with example usage: obj._transform_input(x) and returns: x