class_docstr: Using tf.gradients to implement the backpropagation was
  inspired by the gradient-based implementation approach proposed by Ancona et al, ICLR 2018. Note
  that this package does not currently use the reveal-cancel rule for ReLu units proposed in DeepLIFT.
class_docstr_tok: ["Using","tf.gradients","to","implement","the","backpropagation","was","inspired","by","the","gradient-based","implementation","approach","proposed","by","Ancona","et","al",",","ICLR","2018",".","Note","that","this","package","does","not","currently","use","the","reveal-cancel","rule","for","ReLu","units","proposed","in","DeepLIFT","."]
code: def _init_between_tensors(self, out_op, model_inputs):
      # find all the operations in the graph between our inputs and outputs
      tensor_blacklist = tensors_blocked_by_false(
          self.learning_phase_ops
      )  # don't follow learning phase branches
      dependence_breakers = [
          k for k in op_handlers if op_handlers[k] == break_dependence
      ]
      back_ops = backward_walk_ops(
          [out_op], tensor_blacklist, dependence_breakers
      )
      start_ops = []
      for minput in model_inputs:
          for op in minput.consumers():
              start_ops.append(op)
      self.between_ops = forward_walk_ops(
          start_ops,
          tensor_blacklist,
          dependence_breakers,
          within_ops=back_ops,
      )
  
      # note all the tensors that are on the path between the inputs and the output
      self.between_tensors = {}
      for op in self.between_ops:
          for t in op.outputs:
              self.between_tensors[t.name] = True
      for t in model_inputs:
          self.between_tensors[t.name] = True
  
      # save what types are being used
      self.used_types = {}
      for op in self.between_ops:
          self.used_types[op.type] = True
  
imports: import warnings
  import numpy
  from packaging import version
  from ...utils._exceptions import DimensionError
  from .._explainer import Explainer
  from ..tf_utils import _get_graph, _get_model_inputs, _get_model_output, _get_session
  from .deep_utils import _check_additivity
label_desc: Use image node_id 3 for calling the TFDeep obj's underlying member method code with example usage: obj._init_between_tensors(out_op, model_inputs) without return types