def get(self, record_id: int) -> Dict[str, torch.Tensor]:
        """
        Load tensors from the storage by record ID

        Args:
            record_id (int): Record ID, for which to load the data

        Return:
            dict: str -> tensor: tensor name mapped to tensor data, recorded under the provided ID
        """
        self.storage_impl.seek(record_id * self.record_size_b, os.SEEK_SET)
        data_bytes = self.storage_impl.read(self.record_size_b)
        assert len(data_bytes) == self.record_size_b, (
            f"Expected data size {self.record_size_b} B could not be read: "
            f"got {len(data_bytes)} B"
        )
        record = {}
        cur_idx = 0
        # it's important to read and write in the same order
        for field_name in sorted(self.data_schema):
            schema = self.data_schema[field_name]
            field_size_b = self.record_field_sizes_b[field_name]
            chunk = data_bytes[cur_idx : cur_idx + field_size_b]
            data_np = np.frombuffer(
                chunk, dtype=schema.dtype, count=reduce(mul, schema.shape)
            ).reshape(schema.shape)
            record[field_name] = torch.from_numpy(data_np)
            cur_idx += field_size_b
        return record

        Load tensors from the storage by record ID

        Args:
            record_id (int): Record ID, for which to load the data

        Return:
            dict: str -> tensor: tensor name mapped to tensor data, recorded under the provided ID
        
EVA-02/det/projects/DensePose/densepose/evaluation/tensor_storage.py