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","."]
docstr: The module list is reset for every evaluation, in change the order or number
  of layers changes dynamically.
  
  Returns:
      None
docstr_tok: ["The","module","list","is","reset","for","every","evaluation",",","in","change","the","order","or","number","of","layers","changes","dynamically",".","Returns",":","None"]
code: def reset_module_list(self):
      """
      The module list is reset for every evaluation, in change the order or number
      of layers changes dynamically.
  
      Returns:
          None
  
      """
      self.module_list = []
      # Try to free memory
      if self.device.type == "cuda":
          torch.cuda.empty_cache()
  
imports: import torch
  import torch.nn
  import numpy
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the RelevancePropagator obj's underlying member method code with example usage: obj.reset_module_list() without return types