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 _get_model_output(self, model):
      if len(model.layers[-1]._inbound_nodes) == 0:
          if len(model.outputs) > 1:
              warnings.warn("Only one model output supported.")
          return model.outputs[0]
      else:
          return model.layers[-1].output
  
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 2 for calling the TFDeep obj's underlying member method code with example usage: obj._get_model_output(model) and returns: model, model