docstr: A gradient reversal layer.
  This layer has no parameters, and simply reverses the gradient
  in the backward pass.
docstr_tok: ["A","gradient","reversal","layer",".","This","layer","has","no","parameters",",","and","simply","reverses","the","gradient","in","the","backward","pass","."]
code: def __init__(self, gamma=0.1, gamma_clip=0.4, *args, **kwargs):
      """
      A gradient reversal layer.
      This layer has no parameters, and simply reverses the gradient
      in the backward pass.
      """
      super().__init__(*args, **kwargs)
  
      self.gamma = gamma
      self.gamma_clip = torch.tensor(
          float(gamma_clip), requires_grad=False
      )
      self._alpha = torch.tensor(0, requires_grad=False)
      self._p = 0
  
imports: from __future__ import division
  from __future__ import print_function
  import copy
  import math
  from typing import Text, Union
  import numpy
  import pandas
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from qlib.contrib.model.pytorch_gru import GRUModel
  from qlib.contrib.model.pytorch_lstm import LSTMModel
  from qlib.contrib.model.pytorch_utils import count_parameters
  from qlib.data.dataset import DatasetH
  from qlib.data.dataset.handler import DataHandlerLP
  from qlib.log import get_module_logger
  from qlib.model.base import Model
  from qlib.utils import get_or_create_path
  from torch.autograd import Function
label_desc: Use image node_id 1 to create a new RevGrad object from inherited base classes: nn with example: obj = RevGrad(gamma, gamma_clip)