code: def general_copy_compressed(dst, dst_indices, src, src_indices):
      assert (
          src.device.device_type == DeviceType.COMPRESSED
          and dst.device.device_type == DeviceType.COMPRESSED
      )
  
      src_data_indices, src_scale_indices = get_compressed_indices(
          src, src_indices, src.shape
      )
  
      dst_data_indices, dst_scale_indices = get_compressed_indices(
          dst, dst_indices, dst.shape
      )
  
      general_copy(
          dst.data[0], dst_data_indices, src.data[0], src_data_indices
      )
      general_copy(
          dst.data[1], dst_scale_indices, src.data[1], src_scale_indices
      )
  
imports: import dataclasses
  import torch
  import numpy
  from flexgen.pytorch_backend import TorchTensor, TorchDevice, DeviceType, general_copy, fix_recursive_import
  from flexgen.utils import np_dtype_to_torch_dtype
label_desc: Use image node_id 1 for calling a global function with example usage: general_copy_compressed(dst, dst_indices, src, src_indices) without return types