class_docstr: Class for computing the relevance propagation and supplying
  the necessary forward hooks for all layers.
class_docstr_tok: ["Class","for","computing","the","relevance","propagation","and","supplying","the","necessary","forward","hooks","for","all","layers","."]
code: def __init__(self, lrp_exponent, beta, method, epsilon, device):
      self.device = device
      self.layer = None
      self.p = lrp_exponent
      self.beta = beta
      self.eps = epsilon
      self.warned_log_softmax = False
      self.module_list = []
      if method not in self.available_methods:
          raise NotImplementedError(
              "Only methods available are: "
              + str(self.available_methods)
          )
      self.method = method
  
imports: import torch
  import torch.nn
  import numpy
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new RelevancePropagator object  with example: obj = RelevancePropagator(lrp_exponent, beta, method, epsilon, device)