code: def test_adaptive_compression():
      UINT8 = TrackedCompression(Uniform8BitQuantization())
      FLOAT16 = TrackedCompression(Float16Compression())
      FLOAT32 = TrackedCompression(NoCompression())
      STATE_FP16 = TrackedCompression(Float16Compression())
      STATE_FP32 = TrackedCompression(NoCompression())
  
      averaging_compression_adaptive = RoleAdaptiveCompression(
          parameter=FLOAT16,
          gradient=SizeAdaptiveCompression(
              threshold=1_000, less=FLOAT16, greater_equal=UINT8
          ),
          optimizer=FLOAT32,
          default=FLOAT32,
      )
  
      state_compression_adaptive = SizeAdaptiveCompression(
          threshold=500,
          less=STATE_FP32,
          greater_equal=STATE_FP16,
      )
  
      averager1 = hivemind.TrainingAverager(
          opt=torch.optim.Adam(make_params()),
          average_parameters=True,
          average_gradients=True,
          average_opt_statistics=("exp_avg",),
          compression=averaging_compression_adaptive,
          state_compression=state_compression_adaptive,
          prefix="test_avgr",
          target_group_size=2,
          part_size_bytes=5_000,
          start=True,
          dht=hivemind.DHT(start=True),
      )
  
      averager2 = hivemind.TrainingAverager(
          opt=torch.optim.Adam(make_params()),
          average_parameters=True,
          average_gradients=True,
          average_opt_statistics=("exp_avg",),
          compression=averaging_compression_adaptive,
          state_compression=state_compression_adaptive,
          prefix="test_avgr",
          target_group_size=2,
          part_size_bytes=5_000,
          start=True,
          dht=hivemind.DHT(
              initial_peers=averager1.dht.get_visible_maddrs(),
              start=True,
          ),
      )
  
      futures = [averager1.step(wait=False), averager2.step(wait=False)]
  
      for future in futures:
          future.result()
  
      assert (
          UINT8.mp_counter.value == 4
      )  # half gradients: 3 tensors, 1 is split
      assert UINT8.mp_part_size.value == 5_000  # single byte tensors
      assert (
          FLOAT16.mp_counter.value == 13
      )  # parameters and half gradients
      assert FLOAT16.mp_part_size.value == 2_500  # two-byte tensors
      assert FLOAT32.mp_counter.value == 16  # statistics
      assert FLOAT32.mp_part_size.value == 1250  # four-byte tensors
  
      averager1.load_state_from_peers()
      (
          state_metadata,
          state_tensors,
          infos,
      ) = averager1.get_current_state()
      assert STATE_FP16.mp_counter.value == len(
          [tensor for tensor in state_tensors if tensor.numel() >= 500]
      )
      assert STATE_FP32.mp_counter.value == len(
          [tensor for tensor in state_tensors if tensor.numel() < 500]
      )
      assert (
          STATE_FP16.mp_part_size.value
          == STATE_FP32.mp_part_size.value
          == 0
      )
  
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 8 for calling a global function with example usage: test_adaptive_compression() without return types