code: def __init__(self, in_channels, conv_block=None):
      super(InceptionE, self).__init__()
      conv_block = conv_block or ConvNormAct
      self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)
  
      self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
      self.branch3x3_2a = conv_block(
          384, 384, kernel_size=(1, 3), padding=(0, 1)
      )
      self.branch3x3_2b = conv_block(
          384, 384, kernel_size=(3, 1), padding=(1, 0)
      )
  
      self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
      self.branch3x3dbl_2 = conv_block(
          448, 384, kernel_size=3, padding=1
      )
      self.branch3x3dbl_3a = conv_block(
          384, 384, kernel_size=(1, 3), padding=(0, 1)
      )
      self.branch3x3dbl_3b = conv_block(
          384, 384, kernel_size=(3, 1), padding=(1, 0)
      )
  
      self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
  
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 1 to create a new InceptionE object from inherited base classes: nn with example: obj = InceptionE(in_channels, conv_block)