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):
          for conv, bn in [
              (self.conv1, self.bn1),
              (self.conv2, self.bn2),
              (self.conv3, self.bn3),
          ]:
              x = self.relu(bn(conv(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 typing import Tuple
  from typing import Union
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import nn
  from .layers import AttentionPool2d
  from .layers import Bottleneck
  from .layers import MultiHeadAttention
  from .layers import ResidualAttentionBlock
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