class_docstr: Adds PyTorch specific functions.
class_docstr_tok: ["Adds","PyTorch","specific","functions","."]
docstr: Initialize the (Pytorch specific parts) of the backend engine.
  
  The Pytorch-specifics include setting module class and handling Cuda.
  Also set up the optimizers after the init!
docstr_tok: ["Initialize","the","(","Pytorch","specific","parts",")","of","the","backend","engine",".","The","Pytorch-specifics","include","setting","module","class","and","handling","Cuda",".","Also","set","up","the","optimizers","after","the","init","!"]
code: def __init__(self, base, X, Y, A):
      """
      Initialize the (Pytorch specific parts) of the backend engine.
  
      The Pytorch-specifics include setting module class and handling Cuda.
      Also set up the optimizers after the init!
      """
      global torch
      import torch
  
      torch.manual_seed(base.random_state_.random())
  
      self.model_class = torch.nn.Module
      self.optim_class = torch.optim.Optimizer
      super(PytorchEngine, self).__init__(base, X, Y, A)
  
file_path: fairlearn/fairlearn/adversarial/_pytorch_engine.py
imports: from ._backend_engine import BackendEngine
  from ._constants import _MODEL_UNRECOGNIZED_STR, _MODEL_UNRECOGNIZED_ITEM
label_desc: Use image node_id 1 to create a new PytorchEngine object from inherited base classes: BackendEngine with example: obj = PytorchEngine(base, X, Y, A)