code: def _dist_load_and_train(
      global_rank: int,
      world_size: int,
      gpu_id: int,
      test_dir: Union[str, PathLike],
      mixed_precision: bool,
      **kwargs,
  ):
      # make sure everything is deterministic.
      torch.manual_seed(global_rank)
  
      fsdp_wrapper = FairScaleFsdpAccelerator(
          local_rank=global_rank,
          world_size=world_size,
          cuda_device=gpu_id,
          mixed_precision=mixed_precision,
          **kwargs,
      )
      model = EncoderDecoderModel(fsdp_wrapper)
  
      state_dict: Optional[Dict[str, torch.Tensor]] = None
      if global_rank == 0:
          embedding_weight = torch.randn(12, 4)
          state_dict = {
              "embedding.weight": embedding_weight,
              "emb_proj.weight": torch.randn(4, 4),
              "emb_proj.bias": torch.randn(4),
              "encoder.ff1.linear.weight": torch.randn(4, 4),
              "encoder.ff1.linear.bias": torch.randn(4),
              "encoder.ff2.linear.weight": torch.randn(4, 4),
              "encoder.ff2.linear.bias": torch.randn(4),
              "encoder.buffer": torch.randn(4, 4),
              "decoder.ff.linear.weight": torch.randn(4, 4),
              "decoder.ff.linear.bias": torch.randn(4),
              "decoder.linear.weight": embedding_weight,
              "decoder.buffer": torch.randn(4, 4),
              "buffer": torch.randn(4, 4),
          }
          torch.save(state_dict, os.path.join(test_dir, "state.pt"))
  
      # Make sure the right modules are sharded.
      assert not isinstance(model.embedding, ShardedModuleMixin)
      assert isinstance(model.encoder, ShardedModuleMixin)
      assert isinstance(model.decoder.ff, ShardedModuleMixin)
  
      # Now load the state dict... we should be able to do this before wrapping the model itself
      # with the fsdp_wrapper.
      missing_keys, unexpected_keys = load_state_dict_distributed(
          model, state_dict
      )
      assert not missing_keys
      assert not unexpected_keys
  
      # Make sure weights are still tied.
      model.tie_weights()
  
      # Now wrap outer model.
      model, wrapped_model = fsdp_wrapper.wrap_model(model)
  
      # TODO: grad scaler doesn't work now due to https://github.com/facebookresearch/fairscale/issues/421.
      #  scaler = wrapped_model.init_grad_scaler()
      scaler: Optional[amp.GradScaler] = None
  
      # Checkpoint each worker's state.
      worker_state = wrapped_model.state_dict()
  
      for name, value in worker_state["weights"].items():
          # Each tensor should be on the current device if mixed_precision is `False`,
          # otherwise they will be on CPU (since we set `move_params_to_cpu`).
          if mixed_precision:
              assert value.device == torch.device("cpu")
          else:
              assert value.device == torch.device(gpu_id)
          # Either way, tensors returned should be full precision.
          assert value.dtype == torch.float, f"{name} is {value.dtype}"
  
      # Save state dict from each worker.
      torch.save(
          worker_state,
          os.path.join(test_dir, f"state_worker{gpu_id}.pt"),
      )
  
      # Now we'll make sure we can successfully do a forward pass, backward pass, and optimizer step.
      optim = torch.optim.Adam(
          wrapped_model.model.parameters(), lr=0.0001
      )
      x = torch.randint(12, (2, 6)).to(torch.device(gpu_id))
  
      # Do a forward pass.
      with amp.autocast(enabled=mixed_precision):
          x = wrapped_model.model(x)
          loss = x.sum()
  
      # And a backwards pass + optimizer step.
      if scaler is not None:
          scaler.scale(loss).backward()
          scaler.step(optim)
          scaler.update()
      else:
          loss.backward()
          optim.step()
  
      # Now save final state.
      torch.save(
          wrapped_model.state_dict(),
          os.path.join(test_dir, f"final_state_worker{gpu_id}.pt"),
      )
  
imports: import os
  from os import PathLike
  from typing import Union, Dict, Optional
  import torch
  from torch.cuda import amp
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.testing import AllenNlpTestCase, run_distributed_test, requires_multi_gpu
  from allennlp.nn.util import load_state_dict_distributed
  from allennlp.nn.parallel import FairScaleFsdpAccelerator, FairScaleFsdpWrappedModel, ShardedModuleMixin
label_desc: Use image node_id 1 for calling a global function with example usage: _dist_load_and_train(global_rank, world_size, gpu_id, test_dir, mixed_precision) without return types