class_docstr: Domain classifier network for small-size images
  
  Args:
      input_size (int, optional): the dimension of the final feature vector. Defaults to 128.
      bigger_discrim (bool, optional): whether to use deeper network. Defaults to False.
class_docstr_tok: ["Domain","classifier","network","for","small-size","images","Args",":","input_size","(","int",",","optional",")",":","the","dimension","of","the","final","feature","vector",".","Defaults","to","128.","bigger_discrim","(","bool",",","optional",")",":","whether","to","use","deeper","network",".","Defaults","to","False","."]
code: def forward(self, input):
      x = self.relu1(self.bn1(self.fc1(input)))
      if self.bigger_discrim:
          x = self.relu2(self.bn2(self.fc2(x)))
          x = self.fc3(x)
      else:
          x = self.fc2(x)
      return x
  
imports: import torch
  import torch.nn
  from kale.embed.video_i3d import Unit3D
label_desc: Use image node_id 2 for calling the DomainNetSmallImage obj's underlying member method code with example usage: obj.forward(input) and returns: x