shap_e/models/transmitter/channels_encoder.py [820:853]:
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                tensor = F.interpolate(
                    tensor[None, None],
                    (self.image_size,) * 2,
                    mode="nearest",
                )
                inner_batch.append(tensor.to(device=self.device, dtype=torch.float32))
            tensor_batch.append(torch.cat(inner_batch, dim=0))
        return torch.stack(tensor_batch, dim=0)

    def cameras_to_tensor(
        self, cameras: Union[torch.Tensor, List[List[ProjectiveCamera]]]
    ) -> torch.Tensor:
        """
        Returns a [batch x num_views x 3*4+1] tensor of camera information.
        """
        if isinstance(cameras, torch.Tensor):
            return cameras
        outer_batch = []
        for inner_list in cameras:
            inner_batch = []
            for camera in inner_list:
                inner_batch.append(
                    np.array(
                        [
                            *camera.x,
                            *camera.y,
                            *camera.z,
                            *camera.origin,
                            camera.x_fov,
                        ]
                    )
                )
            outer_batch.append(np.stack(inner_batch, axis=0))
        return torch.from_numpy(np.stack(outer_batch, axis=0)).float()
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