class_docstr: Computes attribution using deconvolution. Deconvolution
  computes the gradient of the target output with respect to the input,
  but gradients of ReLU functions are overridden so that the gradient
  of the ReLU input is simply computed taking ReLU of the output gradient,
  essentially only propagating non-negative gradients (without
  dependence on the sign of the ReLU input).
  
  More details regarding the deconvolution algorithm can be found
  in these papers:
  https://arxiv.org/abs/1311.2901
  https://link.springer.com/chapter/10.1007/978-3-319-46466-4_8
  
  Warning: Ensure that all ReLU operations in the forward function of the
  given model are performed using a module (nn.module.ReLU).
  If nn.functional.ReLU is used, gradients are not overridden appropriately.
class_docstr_tok: ["Computes","attribution","using","deconvolution",".","Deconvolution","computes","the","gradient","of","the","target","output","with","respect","to","the","input",",","but","gradients","of","ReLU","functions","are","overridden","so","that","the","gradient","of","the","ReLU","input","is","simply","computed","taking","ReLU","of","the","output","gradient",",","essentially","only","propagating","non-negative","gradients","(","without","dependence","on","the","sign","of","the","ReLU","input",")",".","More","details","regarding","the","deconvolution","algorithm","can","be","found","in","these","papers",":","https",":","\/\/arxiv.org\/abs\/1311.2901","https",":","\/\/link.springer.com\/chapter\/10.1007\/978-3-319-46466-4_8","Warning",":","Ensure","that","all","ReLU","operations","in","the","forward","function","of","the","given","model","are","performed","using","a","module","(","nn.module.ReLU",")",".","If","nn.functional.ReLU","is","used",",","gradients","are","not","overridden","appropriately","."]
docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which
                  attributions are computed. If model takes a single
                  tensor as input, a single input tensor should be provided.
                  If model takes multiple tensors as input, a tuple
                  of the input tensors should be provided. It is assumed
                  that for all given input tensors, dimension 0 corresponds
                  to the number of examples (aka batch size), and if
                  multiple input tensors are provided, the examples must
                  be aligned appropriately.
      target (int, tuple, Tensor, or list, optional): Output indices for
                  which gradients are computed (for classification cases,
                  this is usually the target class).
                  If the network returns a scalar value per example,
                  no target index is necessary.
                  For general 2D outputs, targets can be either:
  
                  - a single integer or a tensor containing a single
                    integer, which is applied to all input examples
  
                  - a list of integers or a 1D tensor, with length matching
                    the number of examples in inputs (dim 0). Each integer
                    is applied as the target for the corresponding example.
  
                  For outputs with > 2 dimensions, targets can be either:
  
                  - A single tuple, which contains #output_dims - 1
                    elements. This target index is applied to all examples.
  
                  - A list of tuples with length equal to the number of
                    examples in inputs (dim 0), and each tuple containing
                    #output_dims - 1 elements. Each tuple is applied as the
                    target for the corresponding example.
  
                  Default: None
      additional_forward_args (Any, optional): If the forward function
                  requires additional arguments other than the inputs for
                  which attributions should not be computed, this argument
                  can be provided. It must be either a single additional
                  argument of a Tensor or arbitrary (non-tuple) type or a tuple
                  containing multiple additional arguments including tensors
                  or any arbitrary python types. These arguments are provided to
                  model in order, following the arguments in inputs.
                  Note that attributions are not computed with respect
                  to these arguments.
                  Default: None
  
  Returns:
      *Tensor* or *tuple[Tensor, ...]* of **attributions**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  The deconvolution attributions with respect to each
                  input feature. Attributions will always
                  be the same size as the provided inputs, with each value
                  providing the attribution of the corresponding input index.
                  If a single tensor is provided as inputs, a single tensor is
                  returned. If a tuple is provided for inputs, a tuple of
                  corresponding sized tensors is returned.
  
  Examples::
  
      >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
      >>> # and returns an Nx10 tensor of class probabilities.
      >>> net = ImageClassifier()
      >>> deconv = Deconvolution(net)
      >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
      >>> # Computes Deconvolution attribution scores for class 3.
      >>> attribution = deconv.attribute(input, target=3)
docstr_tok: ["Args",":","inputs","(","Tensor","or","tuple","[","Tensor",",","...","]",")",":","Input","for","which","attributions","are","computed",".","If","model","takes","a","single","tensor","as","input",",","a","single","input","tensor","should","be","provided",".","If","model","takes","multiple","tensors","as","input",",","a","tuple","of","the","input","tensors","should","be","provided",".","It","is","assumed","that","for","all","given","input","tensors",",","dimension","0","corresponds","to","the","number","of","examples","(","aka","batch","size",")",",","and","if","multiple","input","tensors","are","provided",",","the","examples","must","be","aligned","appropriately",".","target","(","int",",","tuple",",","Tensor",",","or","list",",","optional",")",":","Output","indices","for","which","gradients","are","computed","(","for","classification","cases",",","this","is","usually","the","target","class",")",".","If","the","network","returns","a","scalar","value","per","example",",","no","target","index","is","necessary",".","For","general","2D","outputs",",","targets","can","be","either",":","-","a","single","integer","or","a","tensor","containing","a","single","integer",",","which","is","applied","to","all","input","examples","-","a","list","of","integers","or","a","1D","tensor",",","with","length","matching","the","number","of","examples","in","inputs","(","dim","0",")",".","Each","integer","is","applied","as","the","target","for","the","corresponding","example",".","For","outputs","with",">","2","dimensions",",","targets","can","be","either",":","-","A","single","tuple",",","which","contains","#","output_dims","-","1","elements",".","This","target","index","is","applied","to","all","examples",".","-","A","list","of","tuples","with","length","equal","to","the","number","of","examples","in","inputs","(","dim","0",")",",","and","each","tuple","containing","#","output_dims","-","1","elements",".","Each","tuple","is","applied","as","the","target","for","the","corresponding","example",".","Default",":","None","additional_forward_args","(","Any",",","optional",")",":","If","the","forward","function","requires","additional","arguments","other","than","the","inputs","for","which","attributions","should","not","be","computed",",","this","argument","can","be","provided",".","It","must","be","either","a","single","additional","argument","of","a","Tensor","or","arbitrary","(","non-tuple",")","type","or","a","tuple","containing","multiple","additional","arguments","including","tensors","or","any","arbitrary","python","types",".","These","arguments","are","provided","to","model","in","order",",","following","the","arguments","in","inputs",".","Note","that","attributions","are","not","computed","with","respect","to","these","arguments",".","Default",":","None","Returns",":","*","Tensor","*","or","*","tuple","[","Tensor",",","...","]","*","of","*","*","attributions","*","*",":","-","*","*","attributions","*","*","(","*","Tensor","*","or","*","tuple","[","Tensor",",","...","]","*",")",":","The","deconvolution","attributions","with","respect","to","each","input","feature",".","Attributions","will","always","be","the","same","size","as","the","provided","inputs",",","with","each","value","providing","the","attribution","of","the","corresponding","input","index",".","If","a","single","tensor","is","provided","as","inputs",",","a","single","tensor","is","returned",".","If","a","tuple","is","provided","for","inputs",",","a","tuple","of","corresponding","sized","tensors","is","returned",".","Examples",":",":",">",">",">","#","ImageClassifier","takes","a","single","input","tensor","of","images","Nx3x32x32",",",">",">",">","#","and","returns","an","Nx10","tensor","of","class","probabilities",".",">",">",">","net","=","ImageClassifier","(",")",">",">",">","deconv","=","Deconvolution","(","net",")",">",">",">","input","=","torch.randn","(","2",",","3",",","32",",","32",",","requires_grad=True",")",">",">",">","#","Computes","Deconvolution","attribution","scores","for","class","3",".",">",">",">","attribution","=","deconv.attribute","(","input",",","target=3",")"]
code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      target: TargetType = None,
      additional_forward_args: Any = None,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which
                      attributions are computed. If model takes a single
                      tensor as input, a single input tensor should be provided.
                      If model takes multiple tensors as input, a tuple
                      of the input tensors should be provided. It is assumed
                      that for all given input tensors, dimension 0 corresponds
                      to the number of examples (aka batch size), and if
                      multiple input tensors are provided, the examples must
                      be aligned appropriately.
          target (int, tuple, Tensor, or list, optional): Output indices for
                      which gradients are computed (for classification cases,
                      this is usually the target class).
                      If the network returns a scalar value per example,
                      no target index is necessary.
                      For general 2D outputs, targets can be either:
  
                      - a single integer or a tensor containing a single
                        integer, which is applied to all input examples
  
                      - a list of integers or a 1D tensor, with length matching
                        the number of examples in inputs (dim 0). Each integer
                        is applied as the target for the corresponding example.
  
                      For outputs with > 2 dimensions, targets can be either:
  
                      - A single tuple, which contains #output_dims - 1
                        elements. This target index is applied to all examples.
  
                      - A list of tuples with length equal to the number of
                        examples in inputs (dim 0), and each tuple containing
                        #output_dims - 1 elements. Each tuple is applied as the
                        target for the corresponding example.
  
                      Default: None
          additional_forward_args (Any, optional): If the forward function
                      requires additional arguments other than the inputs for
                      which attributions should not be computed, this argument
                      can be provided. It must be either a single additional
                      argument of a Tensor or arbitrary (non-tuple) type or a tuple
                      containing multiple additional arguments including tensors
                      or any arbitrary python types. These arguments are provided to
                      model in order, following the arguments in inputs.
                      Note that attributions are not computed with respect
                      to these arguments.
                      Default: None
  
      Returns:
          *Tensor* or *tuple[Tensor, ...]* of **attributions**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      The deconvolution attributions with respect to each
                      input feature. Attributions will always
                      be the same size as the provided inputs, with each value
                      providing the attribution of the corresponding input index.
                      If a single tensor is provided as inputs, a single tensor is
                      returned. If a tuple is provided for inputs, a tuple of
                      corresponding sized tensors is returned.
  
      Examples::
  
          >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
          >>> # and returns an Nx10 tensor of class probabilities.
          >>> net = ImageClassifier()
          >>> deconv = Deconvolution(net)
          >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
          >>> # Computes Deconvolution attribution scores for class 3.
          >>> attribution = deconv.attribute(input, target=3)
      """
      return super().attribute.__wrapped__(
          self, inputs, target, additional_forward_args
      )
  
imports: import warnings
  from typing import Any, List, Tuple, Union
  import torch
  import torch.nn.functional
  from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple, _register_backward_hook
  from captum._utils.gradient import apply_gradient_requirements, undo_gradient_requirements
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.attribution import GradientAttribution
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.hooks import RemovableHandle
label_desc: Use image node_id 2 for calling the Deconvolution obj's underlying member method code with example usage: obj.attribute(inputs, target, additional_forward_args) and returns: super