def _getSmartIndenter(indenterName, qpart, indenter):
    """Get indenter by name.
    Available indenters are none, normal, cstyle, haskell, lilypond, lisp, python, ruby, xml
    Indenter name is not case sensitive
    Raise KeyError if not found
    indentText is indentation, which shall be used. i.e. '\t' for tabs, '    ' for 4 space symbols
    """
    indenterName = indenterName.lower()

    if indenterName in ('haskell', 'lilypond'):  # not supported yet
        logger.warning('Smart indentation for %s not supported yet. But you could be a hero who implemented it' % indenterName)
        from qutepart.indenter.base import IndentAlgNormal as indenterClass
    elif 'none' == indenterName:
        from qutepart.indenter.base import IndentAlgBase as indenterClass
    elif 'normal' == indenterName:
        from qutepart.indenter.base import IndentAlgNormal as indenterClass
    elif 'cstyle' == indenterName:
        from qutepart.indenter.cstyle import IndentAlgCStyle as indenterClass
    elif 'python' == indenterName:
        from qutepart.indenter.python import IndentAlgPython as indenterClass
    elif 'ruby' == indenterName:
        from qutepart.indenter.ruby import IndentAlgRuby as indenterClass
    elif 'xml' == indenterName:
        from qutepart.indenter.xmlindent import IndentAlgXml as indenterClass
    elif 'haskell' == indenterName:
        from qutepart.indenter.haskell import IndenterHaskell as indenterClass
    elif 'lilypond' == indenterName:
        from qutepart.indenter.lilypond import IndenterLilypond as indenterClass
    elif 'lisp' == indenterName:
        from qutepart.indenter.lisp import IndentAlgLisp as indenterClass
    elif 'scheme' == indenterName:
        from qutepart.indenter.scheme import IndentAlgScheme as indenterClass
    else:
        raise KeyError("Indenter %s not found" % indenterName)

    return indenterClass(qpart, indenter)
def shap_values(self, X, ranked_outputs=None, output_rank_order="max", check_additivity=False):

        # X ~ self.model_input
        # X_data ~ self.data

        # check if we have multiple inputs
        if not self.multi_input:
            assert type(X) != list, "Expected a single tensor model input!"
            X = [X]
        else:
            assert type(X) == list, "Expected a list of model inputs!"

        X = [x.detach().to(self.device) for x in X]

        if ranked_outputs is not None and self.multi_output:
            with torch.no_grad():
                model_output_values = self.model(*X)
            # rank and determine the model outputs that we will explain
            if output_rank_order == "max":
                _, model_output_ranks = torch.sort(model_output_values, descending=True)
            elif output_rank_order == "min":
                _, model_output_ranks = torch.sort(model_output_values, descending=False)
            elif output_rank_order == "max_abs":
                _, model_output_ranks = torch.sort(torch.abs(model_output_values), descending=True)
            else:
                assert False, "output_rank_order must be max, min, or max_abs!"
            model_output_ranks = model_output_ranks[:, :ranked_outputs]
        else:
            model_output_ranks = (torch.ones((X[0].shape[0], self.num_outputs)).int() *
                                  torch.arange(0, self.num_outputs).int())

        # add the gradient handles
        handles = self.add_handles(self.model, add_interim_values, deeplift_grad)
        if self.interim:
            self.add_target_handle(self.layer)

        # compute the attributions
        output_phis = []
        for i in range(model_output_ranks.shape[1]):
            phis = []
            if self.interim:
                for k in range(len(self.interim_inputs_shape)):
                    phis.append(np.zeros((X[0].shape[0], ) + self.interim_inputs_shape[k][1: ]))
            else:
                for k in range(len(X)):
                    phis.append(np.zeros(X[k].shape))
            for j in range(X[0].shape[0]):
                # tile the inputs to line up with the background data samples
                tiled_X = [X[l][j:j + 1].repeat(
                                   (self.data[l].shape[0],) + tuple([1 for k in range(len(X[l].shape) - 1)])) for l
                           in range(len(X))]
                joint_x = [torch.cat((tiled_X[l], self.data[l]), dim=0) for l in range(len(X))]
                # run attribution computation graph
                feature_ind = model_output_ranks[j, i]
                sample_phis = self.gradient(feature_ind, joint_x)
                # assign the attributions to the right part of the output arrays
                if self.interim:
                    sample_phis, output = sample_phis
                    x, data = [], []
                    for k in range(len(output)):
                        x_temp, data_temp = np.split(output[k], 2)
                        x.append(x_temp)
                        data.append(data_temp)
                    for l in range(len(self.interim_inputs_shape)):
                        phis[l][j] = (sample_phis[l][self.data[l].shape[0]:] * (x[l] - data[l])).mean(0)
                else:
                    for l in range(len(X)):
                        phis[l][j] = (torch.from_numpy(sample_phis[l][self.data[l].shape[0]:]).to(self.device) * (X[l][j: j + 1] - self.data[l])).cpu().detach().numpy().mean(0)
            output_phis.append(phis[0] if not self.multi_input else phis)
        # cleanup; remove all gradient handles
        for handle in handles:
            handle.remove()
        self.remove_attributes(self.model)
        if self.interim:
            self.target_handle.remove()

        if not self.multi_output:
            return output_phis[0]
        elif ranked_outputs is not None:
            return output_phis, model_output_ranks
        else:
            return output_phis