timm/models/swin_transformer.py [821:876]:
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    def forward_intermediates(
            self,
            x: torch.Tensor,
            indices: Optional[Union[int, List[int]]] = None,
            norm: bool = False,
            stop_early: bool = False,
            output_fmt: str = 'NCHW',
            intermediates_only: bool = False,
    ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
        """Forward features that returns intermediates.

        Args:
            x: Input image tensor.
            indices: Take last n blocks if int, all if None, select matching indices if sequence.
            norm: Apply norm layer to compatible intermediates.
            stop_early: Stop iterating over blocks when last desired intermediate hit.
            output_fmt: Shape of intermediate feature outputs.
            intermediates_only: Only return intermediate features.

        Returns:
            List of intermediate features or tuple of (final features, intermediates).
        """
        assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
        intermediates = []
        take_indices, max_index = feature_take_indices(len(self.layers), indices)

        # forward pass
        x = self.patch_embed(x)

        num_stages = len(self.layers)
        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            stages = self.layers
        else:
            stages = self.layers[:max_index + 1]
        for i, stage in enumerate(stages):
            x = stage(x)
            if i in take_indices:
                if norm and i == num_stages - 1:
                    x_inter = self.norm(x)  # applying final norm last intermediate
                else:
                    x_inter = x
                x_inter = x_inter.permute(0, 3, 1, 2).contiguous()
                intermediates.append(x_inter)

        if intermediates_only:
            return intermediates

        x = self.norm(x)

        return x, intermediates

    def prune_intermediate_layers(
            self,
            indices: Union[int, List[int]] = 1,
            prune_norm: bool = False,
            prune_head: bool = True,
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timm/models/swin_transformer_v2.py [866:920]:
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    def forward_intermediates(
            self,
            x: torch.Tensor,
            indices: Optional[Union[int, List[int]]] = None,
            norm: bool = False,
            stop_early: bool = False,
            output_fmt: str = 'NCHW',
            intermediates_only: bool = False,
    ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
        """ Forward features that returns intermediates.

        Args:
            x: Input image tensor
            indices: Take last n blocks if int, all if None, select matching indices if sequence
            norm: Apply norm layer to compatible intermediates
            stop_early: Stop iterating over blocks when last desired intermediate hit
            output_fmt: Shape of intermediate feature outputs
            intermediates_only: Only return intermediate features
        Returns:

        """
        assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
        intermediates = []
        take_indices, max_index = feature_take_indices(len(self.layers), indices)

        # forward pass
        x = self.patch_embed(x)

        num_stages = len(self.layers)
        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            stages = self.layers
        else:
            stages = self.layers[:max_index + 1]
        for i, stage in enumerate(stages):
            x = stage(x)
            if i in take_indices:
                if norm and i == num_stages - 1:
                    x_inter = self.norm(x)  # applying final norm last intermediate
                else:
                    x_inter = x
                x_inter = x_inter.permute(0, 3, 1, 2).contiguous()
                intermediates.append(x_inter)

        if intermediates_only:
            return intermediates

        x = self.norm(x)

        return x, intermediates

    def prune_intermediate_layers(
            self,
            indices: Union[int, List[int]] = 1,
            prune_norm: bool = False,
            prune_head: bool = True,
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