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 __init__(self, model, data):
        # try and import pytorch
        global torch
        if torch is None:
            import torch
            if version.parse(torch.__version__) < version.parse("0.4"):
                warnings.warn("Your PyTorch version is older than 0.4 and not supported.")

        # check if we have multiple inputs
        self.multi_input = False
        if type(data) == list:
            self.multi_input = True
        if type(data) != list:
            data = [data]
        self.data = data
        self.layer = None
        self.input_handle = None
        self.interim = False
        self.interim_inputs_shape = None
        self.expected_value = None  # to keep the DeepExplainer base happy
        if type(model) == tuple:
            self.interim = True
            model, layer = model
            model = model.eval()
            self.layer = layer
            self.add_target_handle(self.layer)

            # if we are taking an interim layer, the 'data' is going to be the input
            # of the interim layer; we will capture this using a forward hook
            with torch.no_grad():
                _ = model(*data)
                interim_inputs = self.layer.target_input
                if type(interim_inputs) is tuple:
                    # this should always be true, but just to be safe
                    self.interim_inputs_shape = [i.shape for i in interim_inputs]
                else:
                    self.interim_inputs_shape = [interim_inputs.shape]
            self.target_handle.remove()
            del self.layer.target_input
        self.model = model.eval()

        self.multi_output = False
        self.num_outputs = 1
        with torch.no_grad():
            outputs = model(*data)

            # also get the device everything is running on
            self.device = outputs.device
            if outputs.shape[1] > 1:
                self.multi_output = True
                self.num_outputs = outputs.shape[1]
            self.expected_value = outputs.mean(0).cpu().numpy()