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: Evaluates the model on a new input. The registered forward hooks will
  save all the data that is necessary to compute the relevance per neuron per layer.
  
  Args:
      in_tensor: New input for which to predict an output.
  
  Returns:
      Model prediction
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code: def evaluate(self, in_tensor):
      """
      Evaluates the model on a new input. The registered forward hooks will
      save all the data that is necessary to compute the relevance per neuron per layer.
  
      Args:
          in_tensor: New input for which to predict an output.
  
      Returns:
          Model prediction
      """
      # Reset module list. In case the structure changes dynamically,
      # the module list is tracked for every forward pass.
      self.inverter.reset_module_list()
      self.prediction = self.model(in_tensor)
      return self.prediction
  
file_path: tensorwatch/tensorwatch/saliency/epsilon_lrp.py
imports: import torch
  import numpy
  from .inverter_util import RelevancePropagator
label_desc: Use image node_id 7 for calling the InnvestigateModel obj's underlying member method code with example usage: obj.evaluate(in_tensor) and returns: self