code: def test_allreduce_compression():
      """this test ensures that compression works correctly when multiple tensors have different compression types"""
  
      tensors1 = [
          torch.linspace(0, 500, 1000) ** 0.5,
          torch.randn(1000),
      ]
      tensors2 = [
          torch.linspace(300, 800, 1000) ** 0.5,
          torch.randn(1000),
      ]
      results = {}
  
      FLOAT16, UINT8 = Float16Compression(), Uniform8BitQuantization()
  
      for compression_type_pair in [
          (FLOAT16, FLOAT16),
          (FLOAT16, UINT8),
          (UINT8, FLOAT16),
          (UINT8, UINT8),
      ]:
          dht_instances = launch_dht_instances(2)
          averager1 = hivemind.averaging.DecentralizedAverager(
              [x.clone() for x in tensors1],
              dht=dht_instances[0],
              compression=PerTensorCompression(compression_type_pair),
              client_mode=True,
              target_group_size=2,
              prefix="mygroup",
              start=True,
          )
          averager2 = hivemind.averaging.DecentralizedAverager(
              [x.clone() for x in tensors2],
              dht=dht_instances[1],
              compression=PerTensorCompression(compression_type_pair),
              target_group_size=2,
              prefix="mygroup",
              start=True,
          )
  
          for future in averager1.step(wait=False), averager2.step(
              wait=False
          ):
              future.result()
  
          with averager1.get_tensors() as averaged_tensors:
              results[compression_type_pair] = averaged_tensors
  
          for instance in [averager1, averager2] + dht_instances:
              instance.shutdown()
  
      assert torch.allclose(
          results[UINT8, FLOAT16][0], results[UINT8, UINT8][0]
      )
      assert torch.allclose(
          results[UINT8, FLOAT16][1], results[FLOAT16, FLOAT16][1]
      )
      assert torch.allclose(
          results[UINT8, UINT8][1], results[FLOAT16, UINT8][1]
      )
      assert torch.allclose(
          results[FLOAT16, UINT8][0], results[FLOAT16, FLOAT16][0]
      )
  
      assert not torch.allclose(
          results[UINT8, FLOAT16][1], results[UINT8, UINT8][1]
      )
      assert not torch.allclose(
          results[UINT8, FLOAT16][0], results[FLOAT16, FLOAT16][0]
      )
      assert not torch.allclose(
          results[UINT8, UINT8][0], results[FLOAT16, UINT8][0]
      )
      assert not torch.allclose(
          results[FLOAT16, UINT8][1], results[FLOAT16, FLOAT16][1]
      )
  
      reference = [
          (tensors1[i] + tensors2[i]) / 2 for i in range(len(tensors1))
      ]
      for i in range(2):
          assert (
              0
              < torch.mean(
                  torch.square(
                      results[FLOAT16, FLOAT16][i] - reference[i]
                  )
              ).item()
              <= 1e-5
          )
          assert (
              1e-5
              < torch.mean(
                  torch.square(results[UINT8, UINT8][i] - reference[i])
              ).item()
              <= 1e-2
          )
  
imports: import multiprocessing
  from ctypes import c_int32
  import pytest
  import torch
  import torch.nn
  import hivemind
  from hivemind.compression import CompressionBase, CompressionInfo, Float16Compression, NoCompression, PerTensorCompression, RoleAdaptiveCompression, SizeAdaptiveCompression, Uniform8BitQuantization, deserialize_torch_tensor, serialize_torch_tensor
  from hivemind.compression.adaptive import AdaptiveCompressionBase
  from hivemind.proto.runtime_pb2 import CompressionType
  from hivemind.utils.streaming import combine_from_streaming, split_for_streaming
  from test_utils.dht_swarms import launch_dht_instances
label_desc: Use image node_id 6 for calling a global function with example usage: test_allreduce_compression() without return types