class_docstr: A TF1Saliency class that computes saliency masks with GuidedBackProp.
  
  This implementation copies the TensorFlow graph to a new graph with the ReLU
  gradient overwritten as in the paper:
  https://arxiv.org/abs/1412.6806
  
  Thanks to Chris Olah for generously sharing his implementation of the ReLU
  backprop.
docstr: Returns a GuidedBackprop mask.
  
  Args:
    x_value: Input value, not batched.
    feed_dict: (Optional) feed dictionary to pass to the session.run call.
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code: def GetMask(self, x_value, feed_dict=None):
      """Returns a GuidedBackprop mask.
  
      Args:
        x_value: Input value, not batched.
        feed_dict: (Optional) feed dictionary to pass to the session.run call.
      """
      with self.guided_graph.as_default():
          # Move all the feed dict tensor keys to refer to the same tensor on the
          # new graph.
          guided_feed_dict = {}
          for tensor in feed_dict:
              guided_feed_dict[tensor.name] = feed_dict[tensor]
          guided_feed_dict[self.x.name] = [x_value]
  
      return self.guided_sess.run(
          self.guided_grads_node, feed_dict=guided_feed_dict
      )[0]
  
imports: from .base import TF1Saliency
  from .base import TF1Saliency
  from .utils import _import_tf
label_desc: Use image node_id 2 for calling the GuidedBackprop obj's underlying member method code with example usage: obj.GetMask(x_value, feed_dict) and returns: self