class_docstr: Pre-trained ResNetV1b Model, which produces the strides of 8 featuremaps at conv5.
  
  Parameters
  ----------
  block : Block
      Class for the residual block. Options are BasicBlockV1, BottleneckV1.
  layers : list of int
      Numbers of layers in each block
  classes : int, default 1000
      Number of classification classes.
  dilated : bool, default False
      Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
      typically used in Semantic Segmentation.
  norm_layer : object
      Normalization layer used (default: :class:`nn.BatchNorm2d`)
  deep_stem : bool, default False
      Whether to replace the 7x7 conv1 with 3 3x3 convolution layers.
  avg_down : bool, default False
      Whether to use average pooling for projection skip connection between stages/downsample.
  final_drop : float, default 0.0
      Dropout ratio before the final classification layer.
  
  Reference:
      - He, Kaiming, et al. "Deep residual learning for image recognition."
      Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  
      - Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
class_docstr_tok: ["Pre-trained","ResNetV1b","Model",",","which","produces","the","strides","of","8","featuremaps","at","conv5",".","Parameters","--","--","--","--","--","block",":","Block","Class","for","the","residual","block",".","Options","are","BasicBlockV1",",","BottleneckV1",".","layers",":","list","of","int","Numbers","of","layers","in","each","block","classes",":","int",",","default","1000","Number","of","classification","classes",".","dilated",":","bool",",","default","False","Applying","dilation","strategy","to","pretrained","ResNet","yielding","a","stride-8","model",",","typically","used","in","Semantic","Segmentation",".","norm_layer",":","object","Normalization","layer","used","(","default",":",":","class",":","`","nn.BatchNorm2d","`",")","deep_stem",":","bool",",","default","False","Whether","to","replace","the","7x7","conv1","with","3","3x3","convolution","layers",".","avg_down",":","bool",",","default","False","Whether","to","use","average","pooling","for","projection","skip","connection","between","stages\/downsample",".","final_drop",":","float",",","default","0.0","Dropout","ratio","before","the","final","classification","layer",".","Reference",":","-","He",",","Kaiming",",","et","al",".","``","Deep","residual","learning","for","image","recognition",".","''","Proceedings","of","the","IEEE","conference","on","computer","vision","and","pattern","recognition",".","2016",".","-","Yu",",","Fisher",",","and","Vladlen","Koltun",".","``","Multi-scale","context","aggregation","by","dilated","convolutions",".","''"]
code: def forward(self, x):
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.maxpool(x)
  
      x = self.layer1(x)
      x = self.layer2(x)
      x = self.layer3(x)
      x = self.layer4(x)
  
      x = self.avgpool(x)
      x = x.view(x.size(0), -1)
      if self.drop is not None:
          x = self.drop(x)
      x = self.fc(x)
  
      return x
  
file_path: carefree-learn/cflearn/api/cv/third_party/iharm/model/modeling/resnetv1b.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the ResNetV1b obj's underlying member method code with example usage: obj.forward(x) and returns: x