How to use the captum.attr._utils.attribution.GradientAttribution.__init__ function in captum

forward_func (callable):  The forward function of the model or any
                          modification of it
            layer (torch.nn.Module): Layer for which attributions are computed.
                          Output size of attribute matches this layer's output
                          dimensions, except for dimension 2, which will be 1,
                          since GradCAM sums over channels.
                          Currently, only layers with a single tensor output are
                          supported.
            device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model. This allows reconstruction of
                          intermediate outputs from batched results across devices.
                          If forward_func is given as the DataParallel model itself,
                          then it is not necessary to provide this argument.
        """
        LayerAttribution.__init__(self, forward_func, layer, device_ids)
        GradientAttribution.__init__(self, forward_func)

def __init__(self, forward_func):
        r"""
        Args:

            forward_func (callable):  The forward function of the model or any
                          modification of it
        """
        GradientAttribution.__init__(self, forward_func)

def __init__(self, forward_func):
        r"""
        Args:

            forward_func (callable):  The forward function of the model or any
                          modification of it
        """
        GradientAttribution.__init__(self, forward_func)

def __init__(self, forward_func):
        r"""
        Args:

            forward_func (function): The forward function of the model or
                       any modification of it
        """
        GradientAttribution.__init__(self, forward_func)

Output size of attribute matches this layer's input or
                          output dimensions, depending on whether we attribute to
                          the inputs or outputs of the layer, corresponding to
                          attribution of each neuron in the input or output of
                          this layer.
                          Currently, it is assumed that the inputs or the outputs
                          of the layer, depending on which one is used for
                          attribution, can only be a single tensor.
            device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model. This allows reconstruction of
                          intermediate outputs from batched results across devices.
                          If forward_func is given as the DataParallel model itself,
                          then it is not necessary to provide this argument.
        """
        NeuronAttribution.__init__(self, forward_func, layer, device_ids)
        GradientAttribution.__init__(self, forward_func)

Output size of attribute matches this layer's input or
                          output dimensions, depending on whether we attribute to
                          the inputs or outputs of the layer, corresponding to
                          attribution of each neuron in the input or output of
                          this layer.
                          Currently, it is assumed that the inputs or the outputs
                          of the layer, depending on which one is used for
                          attribution, can only be a single tensor.
            device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model. This allows reconstruction of
                          intermediate outputs from batched results across devices.
                          If forward_func is given as the DataParallel model itself,
                          then it is not necessary to provide this argument.
        """
        LayerAttribution.__init__(self, forward_func, layer, device_ids)
        GradientAttribution.__init__(self, forward_func)

def __init__(self, forward_func):
        r"""
        Args:

            forward_func (callable): The forward function of the model or
                        any modification of it
        """
        GradientAttribution.__init__(self, forward_func)

Output size of attribute matches this layer's input or
                          output dimensions, depending on whether we attribute to
                          the inputs or outputs of the layer, corresponding to
                          attribution of each neuron in the input or output of
                          this layer.
                          Currently, it is assumed that the inputs or the outputs
                          of the layer, depending on which one is used for
                          attribution, can only be a single tensor.
            device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model. This allows reconstruction of
                          intermediate outputs from batched results across devices.
                          If forward_func is given as the DataParallel model itself,
                          then it is not necessary to provide this argument.
        """
        NeuronAttribution.__init__(self, model, layer, device_ids)
        GradientAttribution.__init__(self, model)
        self.deconv = Deconvolution(model)

Output size of attribute matches this layer's input or
                          output dimensions, depending on whether we attribute to
                          the inputs or outputs of the layer, corresponding to
                          attribution of each neuron in the input or output of
                          this layer.
                          Currently, it is assumed that the inputs or the outputs
                          of the layer, depending on which one is used for
                          attribution, can only be a single tensor.
            device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model. This allows reconstruction of
                          intermediate outputs from batched results across devices.
                          If forward_func is given as the DataParallel model itself,
                          then it is not necessary to provide this argument.
        """
        LayerAttribution.__init__(self, forward_func, layer, device_ids)
        GradientAttribution.__init__(self, forward_func)

def __init__(self, model):
        r"""
        Args:

            model (nn.Module):  The reference to PyTorch model instance.
        """
        GradientAttribution.__init__(self, model)
        if isinstance(model, nn.DataParallel):
            warnings.warn(
                """Although input model is of type `nn.DataParallel` it will run
                only on one device. Support for multiple devices will be added soon."""
            )
            self.model = model.module
        else:
            self.model = model
        self.forward_handles = []
        self.forward_handles_refs = []
        self.backward_handles = []