class_docstr: ResNet with the softmax chopped off and the batchnorm frozen
class_docstr_tok: ["ResNet","with","the","softmax","chopped","off","and","the","batchnorm","frozen"]
docstr: Encode x into a feature vector of size n_outputs.
docstr_tok: ["Encode","x","into","a","feature","vector","of","size","n_outputs","."]
code: def forward(self, x):
      """Encode x into a feature vector of size n_outputs."""
      return self.dropout(self.network(x))
  
imports: import copy
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision.models
  from domainbed.lib import wide_resnet
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj.forward(x) and returns: self