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 __init__(self, input_size=128, bigger_discrim=False):
      super(DomainNetSmallImage, self).__init__()
      output_size = 500 if bigger_discrim else 100
  
      self.bigger_discrim = bigger_discrim
      self.fc1 = nn.Linear(input_size, output_size)
      self.bn1 = nn.BatchNorm1d(output_size)
      self.relu1 = nn.ReLU()
      self.fc2 = (
          nn.Linear(output_size, 100)
          if bigger_discrim
          else nn.Linear(output_size, 2)
      )
      self.bn2 = nn.BatchNorm1d(100)
      self.relu2 = nn.ReLU()
      self.fc3 = nn.Linear(100, 2)
  
imports: import torch
  import torch.nn
  from kale.embed.video_i3d import Unit3D
label_desc: Use image node_id 1 to create a new DomainNetSmallImage object from inherited base classes: nn with example: obj = DomainNetSmallImage(input_size, bigger_discrim)