captum/influence/_core/tracincp.py [539:638]:
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    @log_usage()
    def influence(  # type: ignore[override]
        self,
        inputs: Any = None,
        targets: Optional[Tensor] = None,
        k: Optional[int] = None,
        proponents: bool = True,
        unpack_inputs: bool = True,
    ) -> Union[Tensor, KMostInfluentialResults]:
        r"""
        This is the key method of this class, and can be run in 3 different modes,
        where the mode that is run depends on the arguments passed to this method:

        - self influence mode: This mode is used if `inputs` is None. This mode
          computes the self influence scores for every example in
          the training dataset `influence_src_dataset`.
        - influence score mode: This mode is used if `inputs` is not None, and `k` is
          None. This mode computes the influence score of every example in
          training dataset `influence_src_dataset` on every example in the test
          batch represented by `inputs` and `targets`.
        - k-most influential mode: This mode is used if `inputs` is not None, and
          `k` is not None, and an int. This mode computes the proponents or
          opponents of every example in the test batch represented by `inputs`
          and `targets`. In particular, for each test example in the test batch,
          this mode computes its proponents (resp. opponents), which are the
          indices in the training dataset `influence_src_dataset` of the training
          examples with the `k` highest (resp. lowest) influence scores on the
          test example. Proponents are computed if `proponents` is True.
          Otherwise, opponents are computed. For each test example, this method
          also returns the actual influence score of each proponent (resp.
          opponent) on the test example.

        Args:
            inputs (Any, optional): If not provided or `None`, the self influence mode
                    will be run. Otherwise, `inputs` is the test batch that will be
                    used when running in either influence score or k-most influential
                    mode. If the argument `unpack_inputs` is False, the
                    assumption is that `self.model(inputs)` produces the predictions
                    for a batch, and `inputs` can be of any type. Otherwise if the
                    argument `unpack_inputs` is True, the assumption is that
                    `self.model(*inputs)` produces the predictions for a batch, and
                    `inputs` will need to be a tuple. In other words, `inputs` will be
                    unpacked as an argument when passing to `self.model`.
                    Default: None
            targets (tensor, optional): If computing influence scores on a loss
                    function, these are the labels corresponding to the batch `inputs`.
                    Default: None
            k (int, optional): If not provided or `None`, the influence score mode will
                    be run. Otherwise, the k-most influential mode will be run,
                    and `k` is the number of proponents / opponents to return per
                    example in the test batch.
                    Default: None
            proponents (bool, optional): Whether seeking proponents (`proponents=True`)
                    or opponents (`proponents=False`), if running in k-most influential
                    mode.
                    Default: True
            unpack_inputs (bool, optional): Whether to unpack the `inputs` argument to
                    when passing it to `model`, if `inputs` is a tuple (no unpacking
                    done otherwise).
                    Default: True

        Returns:
            The return value of this method depends on which mode is run.

            - self influence mode: if this mode is run (`inputs` is None), returns a 1D
              tensor of self influence scores over training dataset
              `influence_src_dataset`. The length of this tensor is the number of
              examples in `influence_src_dataset`, regardless of whether it is a
              Dataset or DataLoader.
            - influence score mode: if this mode is run (`inputs is not None, `k` is
              None), returns a 2D tensor `influence_scores` of shape
              `(input_size, influence_src_dataset_size)`, where `input_size` is
              the number of examples in the test batch, and
              `influence_src_dataset_size` is the number of examples in
              training dataset `influence_src_dataset`. In other words,
              `influence_scores[i][j]` is the influence score of the `j`-th
              example in `influence_src_dataset` on the `i`-th example in the
              test batch.
            - k-most influential mode: if this mode is run (`inputs` is not None,
              `k` is an int), returns a namedtuple `(indices, influence_scores)`.
              `indices` is a 2D tensor of shape `(input_size, k)`, where
              `input_size` is the number of examples in the test batch. If
              computing proponents (resp. opponents), `indices[i][j]` is the
              index in training dataset `influence_src_dataset` of the example
              with the `j`-th highest (resp. lowest) influence score (out of the
              examples in `influence_src_dataset`) on the `i`-th example in the
              test batch. `influence_scores` contains the corresponding influence
              scores. In particular, `influence_scores[i][j]` is the influence
              score of example `indices[i][j]` in `influence_src_dataset` on
              example `i` in the test batch represented by `inputs` and
              `targets`.
        """
        return _influence_route_to_helpers(
            self,
            inputs,
            targets,
            k,
            proponents,
            unpack_inputs,
        )
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captum/influence/_core/tracincp_fast_rand_proj.py [192:292]:
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    @log_usage()
    def influence(  # type: ignore[override]
        self,
        inputs: Any = None,
        targets: Optional[Tensor] = None,
        k: Optional[int] = None,
        proponents: bool = True,
        unpack_inputs: bool = True,
    ) -> Union[Tensor, KMostInfluentialResults]:
        r"""
        This is the key method of this class, and can be run in 3 different modes,
        where the mode that is run depends on the arguments passed to this method:

        - self influence mode: This mode is used if `inputs` is None. This mode
          computes the self influence scores for every example in
          the training dataset `influence_src_dataset`.
        - influence score mode: This mode is used if `inputs` is not None, and `k` is
          None. This mode computes the influence score of every example in
          training dataset `influence_src_dataset` on every example in the test
          batch represented by `inputs` and `targets`.
        - k-most influential mode: This mode is used if `inputs` is not None, and
          `k` is not None, and an int. This mode computes the proponents or
          opponents of every example in the test batch represented by `inputs`
          and `targets`. In particular, for each test example in the test batch,
          this mode computes its proponents (resp. opponents), which are the
          indices in the training dataset `influence_src_dataset` of the training
          examples with the `k` highest (resp. lowest) influence scores on the
          test example. Proponents are computed if `proponents` is True.
          Otherwise, opponents are computed. For each test example, this method
          also returns the actual influence score of each proponent (resp.
          opponent) on the test example.

        Args:
            inputs (Any, optional): If not provided or `None`, the self influence mode
                    will be run. Otherwise, `inputs` is the test batch that will be
                    used when running in either influence score or k-most influential
                    mode. If the argument `unpack_inputs` is False, the
                    assumption is that `self.model(inputs)` produces the predictions
                    for a batch, and `inputs` can be of any type. Otherwise if the
                    argument `unpack_inputs` is True, the assumption is that
                    `self.model(*inputs)` produces the predictions for a batch, and
                    `inputs` will need to be a tuple. In other words, `inputs` will be
                    unpacked as an argument when passing to `self.model`.
                    Default: None
            targets (tensor, optional): The labels corresponding to the batch `inputs`.
                    This method is designed to be applied for a loss function, so
                    `targets` is required, unless running in "self influence" mode.
                    Default: None
            k (int, optional): If not provided or `None`, the influence score mode will
                    be run. Otherwise, the k-most influential mode will be run,
                    and `k` is the number of proponents / opponents to return per
                    example in the test batch.
                    Default: None
            proponents (bool, optional): Whether seeking proponents (`proponents=True`)
                    or opponents (`proponents=False`), if running in k-most influential
                    mode.
                    Default: True
            unpack_inputs (bool, optional): Whether to unpack the `inputs` argument to
                    when passing it to `model`, if `inputs` is a tuple (no unpacking
                    done otherwise).
                    Default: True

        Returns:
            The return value of this method depends on which mode is run.

            - self influence mode: if this mode is run (`inputs` is None), returns a 1D
              tensor of self influence scores over training dataset
              `influence_src_dataset`. The length of this tensor is the number of
              examples in `influence_src_dataset`, regardless of whether it is a
              Dataset or DataLoader.
            - influence score mode: if this mode is run (`inputs is not None, `k` is
              None), returns a 2D tensor `influence_scores` of shape
              `(input_size, influence_src_dataset_size)`, where `input_size` is
              the number of examples in the test batch, and
              `influence_src_dataset_size` is the number of examples in
              training dataset `influence_src_dataset`. In other words,
              `influence_scores[i][j]` is the influence score of the `j`-th
              example in `influence_src_dataset` on the `i`-th example in the
              test batch.
            - k-most influential mode: if this mode is run (`inputs` is not None,
              `k` is an int), returns a namedtuple `(indices, influence_scores)`.
              `indices` is a 2D tensor of shape `(input_size, k)`, where
              `input_size` is the number of examples in the test batch. If
              computing proponents (resp. opponents), `indices[i][j]` is the
              index in training dataset `influence_src_dataset` of the example
              with the `j`-th highest (resp. lowest) influence score (out of the
              examples in `influence_src_dataset`) on the `i`-th example in the
              test batch. `influence_scores` contains the corresponding influence
              scores. In particular, `influence_scores[i][j]` is the influence
              score of example `indices[i][j]` in `influence_src_dataset` on
              example `i` in the test batch represented by `inputs` and
              `targets`.
        """
        return _influence_route_to_helpers(
            self,
            inputs,
            targets,
            k,
            proponents,
            unpack_inputs,
        )
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