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()