class_docstr: A ResNet class that is similar to torchvision's but contains the following changes:
  - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
  - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
  - The final pooling layer is a QKV attention instead of an average pool
class_docstr_tok: ["A","ResNet","class","that","is","similar","to","torchvision","'s","but","contains","the","following","changes",":","-","There","are","now","3","``","stem","''","convolutions","as","opposed","to","1",",","with","an","average","pool","instead","of","a","max","pool",".","-","Performs","anti-aliasing","strided","convolutions",",","where","an","avgpool","is","prepended","to","convolutions","with","stride",">","1","-","The","final","pooling","layer","is","a","QKV","attention","instead","of","an","average","pool"]
code: def forward(self, x):
      def stem(x):
          x = self.relu1(self.bn1(self.conv1(x)))
          x = self.relu2(self.bn2(self.conv2(x)))
          x = self.relu3(self.bn3(self.conv3(x)))
          x = self.avgpool(x)
          return x
  
      x = x.type(self.conv1.weight.dtype)
      x = stem(x)
      x = self.layer1(x)
      x = self.layer2(x)
      x = self.layer3(x)
      x = self.layer4(x)
      x = self.attnpool(x)
  
      return x
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 3 for calling the ModifiedResNet obj's underlying member method code with example usage: obj.forward(x) and returns: x, x