timm/models/efficientnet.py [244:271]:
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        intermediates = []
        if extra_blocks:
            take_indices, max_index = feature_take_indices(len(self.blocks) + 1, indices)
        else:
            take_indices, max_index = feature_take_indices(len(self.stage_ends), indices)
            take_indices = [self.stage_ends[i] for i in take_indices]
            max_index = self.stage_ends[max_index]
        # forward pass
        feat_idx = 0  # stem is index 0
        x = self.conv_stem(x)
        x = self.bn1(x)
        if feat_idx in take_indices:
            intermediates.append(x)

        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            blocks = self.blocks
        else:
            blocks = self.blocks[:max_index]
        for feat_idx, blk in enumerate(blocks, start=1):
            if self.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint_seq(blk, x)
            else:
                x = blk(x)
            if feat_idx in take_indices:
                intermediates.append(x)

        if intermediates_only:
            return intermediates
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timm/models/mobilenetv3.py [211:239]:
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        intermediates = []
        if extra_blocks:
            take_indices, max_index = feature_take_indices(len(self.blocks) + 1, indices)
        else:
            take_indices, max_index = feature_take_indices(len(self.stage_ends), indices)
            take_indices = [self.stage_ends[i] for i in take_indices]
            max_index = self.stage_ends[max_index]

        # forward pass
        feat_idx = 0  # stem is index 0
        x = self.conv_stem(x)
        x = self.bn1(x)
        if feat_idx in take_indices:
            intermediates.append(x)

        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            blocks = self.blocks
        else:
            blocks = self.blocks[:max_index]
        for feat_idx, blk in enumerate(blocks, start=1):
            if self.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint_seq(blk, x)
            else:
                x = blk(x)
            if feat_idx in take_indices:
                intermediates.append(x)

        if intermediates_only:
            return intermediates
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