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","."]
docstr: Return which inputs of this operation are variable (i.e. depend on the model inputs).
          
docstr_tok: ["Return","which","inputs","of","this","operation","are","variable","(","i.e",".","depend","on","the","model","inputs",")","."]
code: def _variable_inputs(self, op):
      """Return which inputs of this operation are variable (i.e. depend on the model inputs)."""
      if op not in self._vinputs:
          out = np.zeros(len(op.inputs), dtype=bool)
          for i, t in enumerate(op.inputs):
              out[i] = t.name in self.between_tensors
          self._vinputs[op] = out
      return self._vinputs[op]
  
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 4 for calling the TFDeep obj's underlying member method code with example usage: obj._variable_inputs(op) and returns: self