class_docstr: ATTENTION:
      Currently, innvestigating a network only works if all
      layers that have to be inverted are specified explicitly
      and registered as a module. If., for example,
      only the functional max_poolnd is used, the inversion will not work.
class_docstr_tok: ["ATTENTION",":","Currently",",","innvestigating","a","network","only","works","if","all","layers","that","have","to","be","inverted","are","specified","explicitly","and","registered","as","a","module",".","If.",",","for","example",",","only","the","functional","max_poolnd","is","used",",","the","inversion","will","not","work","."]
docstr: The innvestigate wrapper returns the same prediction as the
  original model, but wraps the model call method in the evaluate
  method to save the last prediction.
  
  Args:
      in_tensor: Model input to pass through the pytorch model.
  
  Returns:
      Model output.
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code: def __call__(self, in_tensor):
      """
      The innvestigate wrapper returns the same prediction as the
      original model, but wraps the model call method in the evaluate
      method to save the last prediction.
  
      Args:
          in_tensor: Model input to pass through the pytorch model.
  
      Returns:
          Model output.
      """
      return self.evaluate(in_tensor)
  
file_path: tensorwatch/tensorwatch/saliency/epsilon_lrp.py
imports: import torch
  import numpy
  from .inverter_util import RelevancePropagator
label_desc: Use image node_id 6 for calling the InnvestigateModel obj's underlying member method code with example usage: obj.__call__(in_tensor) and returns: self