shap_e/models/transmitter/channels_encoder.py [739:759]:
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            inner_batch = []
            for img in inner_list:
                img = img.resize((self.image_size,) * 2).convert("RGB")
                inner_batch.append(
                    torch.from_numpy(np.array(img)).to(device=self.device, dtype=torch.float32)
                    / 127.5
                    - 1
                )
            tensor_batch.append(torch.stack(inner_batch, dim=0))
        return torch.stack(tensor_batch, dim=0).permute(0, 1, 4, 2, 3)

    def depths_to_tensor(
        self, depths: Union[torch.Tensor, List[List[Image.Image]]]
    ) -> torch.Tensor:
        """
        Returns a [batch x num_views x 1 x size x size] tensor in the range [-1, 1].
        """
        if isinstance(depths, torch.Tensor):
            return depths

        tensor_batch = []
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shap_e/models/transmitter/multiview_encoder.py [141:161]:
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            inner_batch = []
            for img in inner_list:
                img = img.resize((self.image_size,) * 2).convert("RGB")
                inner_batch.append(
                    torch.from_numpy(np.array(img)).to(device=self.device, dtype=torch.float32)
                    / 127.5
                    - 1
                )
            tensor_batch.append(torch.stack(inner_batch, dim=0))
        return torch.stack(tensor_batch, dim=0).permute(0, 1, 4, 2, 3)

    def depths_to_tensor(
        self, depths: Union[torch.Tensor, List[List[Image.Image]]]
    ) -> torch.Tensor:
        """
        Returns a [batch x num_views x 1 x size x size] tensor in the range [-1, 1].
        """
        if isinstance(depths, torch.Tensor):
            return depths

        tensor_batch = []
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