code: def _forward(self, x):
      branch3x3 = self.branch3x3_1(x)
      branch3x3 = self.branch3x3_2(branch3x3)
  
      branch7x7x3 = self.branch7x7x3_1(x)
      branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
      branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
      branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
  
      branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
      outputs = [branch3x3, branch7x7x3, branch_pool]
      return outputs
  
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import trunc_normal_, create_classifier, Linear, ConvNormAct
  from ._builder import build_model_with_cfg
  from ._builder import resolve_pretrained_cfg
  from ._manipulate import flatten_modules
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 2 for calling the InceptionD obj's underlying member method code with example usage: obj._forward(x) and returns: outputs