timm/models/davit.py [677:710]:
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                x = stage(x)
            if feat_idx in take_indices:
                if norm and feat_idx == last_idx:
                    x_inter = self.norm_pre(x)  # applying final norm to last intermediate
                else:
                    x_inter = x
                intermediates.append(x_inter)

        if intermediates_only:
            return intermediates

        if feat_idx == last_idx:
            x = self.norm_pre(x)

        return x, intermediates

    def prune_intermediate_layers(
            self,
            indices: Union[int, List[int]] = 1,
            prune_norm: bool = False,
            prune_head: bool = True,
    ):
        """ Prune layers not required for specified intermediates.
        """
        take_indices, max_index = feature_take_indices(len(self.stages), indices)
        self.stages = self.stages[:max_index + 1]  # truncate blocks w/ stem as idx 0
        if prune_norm:
            self.norm_pre = nn.Identity()
        if prune_head:
            self.reset_classifier(0, '')
        return take_indices

    def forward_features(self, x):
        x = self.stem(x)
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timm/models/edgenext.py [456:489]:
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            x = stage(x)
            if feat_idx in take_indices:
                if norm and feat_idx == last_idx:
                    x_inter = self.norm_pre(x)  # applying final norm to last intermediate
                else:
                    x_inter = x
                intermediates.append(x_inter)

        if intermediates_only:
            return intermediates

        if feat_idx == last_idx:
            x = self.norm_pre(x)

        return x, intermediates

    def prune_intermediate_layers(
            self,
            indices: Union[int, List[int]] = 1,
            prune_norm: bool = False,
            prune_head: bool = True,
    ):
        """ Prune layers not required for specified intermediates.
        """
        take_indices, max_index = feature_take_indices(len(self.stages), indices)
        self.stages = self.stages[:max_index + 1]  # truncate blocks w/ stem as idx 0
        if prune_norm:
            self.norm_pre = nn.Identity()
        if prune_head:
            self.reset_classifier(0, '')
        return take_indices

    def forward_features(self, x):
        x = self.stem(x)
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