code: def training_check(use_seedable_sampler=False):
      state = AcceleratorState()
      generator = torch.Generator()
      batch_size = 8
      length = batch_size * 4 * state.num_processes
  
      train_set, old_model = mock_training(
          length,
          batch_size * state.num_processes,
          generator,
          use_seedable_sampler,
      )
      assert are_the_same_tensors(
          old_model.a
      ), "Did not obtain the same model on both processes."
      assert are_the_same_tensors(
          old_model.b
      ), "Did not obtain the same model on both processes."
  
      accelerator = Accelerator()
      if use_seedable_sampler:
          # The SeedableRandomSampler is needed during distributed setups
          # for full reproducability across processes with the `DataLoader`
          sampler = SeedableRandomSampler(
              generator=generator,
              data_source=train_set,
              num_samples=len(train_set),
          )
          train_dl = DataLoader(
              train_set, batch_size=batch_size, sampler=sampler
          )
      else:
          train_dl = DataLoader(
              train_set,
              batch_size=batch_size,
              shuffle=True,
              generator=generator,
          )
      model = RegressionModel()
      optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
  
      train_dl, model, optimizer = accelerator.prepare(
          train_dl, model, optimizer
      )
      set_seed(42)
      generator.manual_seed(42)
      for epoch in range(3):
          for batch in train_dl:
              model.zero_grad()
              output = model(batch["x"])
              loss = torch.nn.functional.mse_loss(output, batch["y"])
              accelerator.backward(loss)
              optimizer.step()
  
      model = accelerator.unwrap_model(model).cpu()
      assert torch.allclose(
          old_model.a, model.a
      ), "Did not obtain the same model on CPU or distributed training."
      assert torch.allclose(
          old_model.b, model.b
      ), "Did not obtain the same model on CPU or distributed training."
  
      accelerator.print(
          "Training yielded the same results on one CPU or distributed setup with no batch split."
      )
  
      accelerator = Accelerator(
          split_batches=True, use_seedable_sampler=use_seedable_sampler
      )
      train_dl = DataLoader(
          train_set,
          batch_size=batch_size * state.num_processes,
          shuffle=True,
          generator=generator,
      )
      model = RegressionModel()
      optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
  
      train_dl, model, optimizer = accelerator.prepare(
          train_dl, model, optimizer
      )
      set_seed(42)
      generator.manual_seed(42)
      for _ in range(3):
          for batch in train_dl:
              model.zero_grad()
              output = model(batch["x"])
              loss = torch.nn.functional.mse_loss(output, batch["y"])
              accelerator.backward(loss)
              optimizer.step()
  
      model = accelerator.unwrap_model(model).cpu()
      assert torch.allclose(
          old_model.a, model.a
      ), "Did not obtain the same model on CPU or distributed training."
      assert torch.allclose(
          old_model.b, model.b
      ), "Did not obtain the same model on CPU or distributed training."
  
      accelerator.print(
          "Training yielded the same results on one CPU or distributes setup with batch split."
      )
  
      if torch.cuda.is_available() or is_npu_available():
          # Mostly a test that FP16 doesn't crash as the operation inside the model is not converted to FP16
          print("FP16 training check.")
          AcceleratorState._reset_state()
          accelerator = Accelerator(
              mixed_precision="fp16",
              use_seedable_sampler=use_seedable_sampler,
          )
          train_dl = DataLoader(
              train_set,
              batch_size=batch_size,
              shuffle=True,
              generator=generator,
          )
          model = RegressionModel()
          optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
  
          train_dl, model, optimizer = accelerator.prepare(
              train_dl, model, optimizer
          )
          set_seed(42)
          generator.manual_seed(42)
          for _ in range(3):
              for batch in train_dl:
                  model.zero_grad()
                  output = model(batch["x"])
                  loss = torch.nn.functional.mse_loss(
                      output, batch["y"]
                  )
                  accelerator.backward(loss)
                  optimizer.step()
  
          model = accelerator.unwrap_model(model).cpu()
          assert torch.allclose(
              old_model.a, model.a
          ), "Did not obtain the same model on CPU or distributed training."
          assert torch.allclose(
              old_model.b, model.b
          ), "Did not obtain the same model on CPU or distributed training."
  
      if torch.cuda.is_available():
          # Mostly a test that model.forward will have autocast when running unwrap_model(model, keep_fp32_wrapper=True)
          print("Keep fp32 wrapper check.")
          AcceleratorState._reset_state()
          accelerator = Accelerator(mixed_precision="fp16")
  
          model = torch.nn.Linear(2, 4)
          model = accelerator.prepare(model)
          model_with_fp32_wrapper = accelerator.unwrap_model(
              model, keep_fp32_wrapper=True
          )
  
          # Run forward with fp16 as input.
          # When the model is with mixed precision wrapper, no error will be raised.
          input_tensor = torch.Tensor([1, 2]).to(
              dtype=torch.float16, device=accelerator.device
          )
          output = model_with_fp32_wrapper(input_tensor)
  
      # BF16 support is only for CPU + TPU, and some GPU
      if is_bf16_available():
          # Mostly a test that BF16 doesn't crash as the operation inside the model is not converted to BF16
          print("BF16 training check.")
          AcceleratorState._reset_state()
          accelerator = Accelerator(
              mixed_precision="bf16",
              use_seedable_sampler=use_seedable_sampler,
          )
          train_dl = DataLoader(
              train_set,
              batch_size=batch_size,
              shuffle=True,
              generator=generator,
          )
          model = RegressionModel()
          optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
  
          train_dl, model, optimizer = accelerator.prepare(
              train_dl, model, optimizer
          )
          set_seed(42)
          generator.manual_seed(42)
          for _ in range(3):
              for batch in train_dl:
                  model.zero_grad()
                  output = model(batch["x"])
                  loss = torch.nn.functional.mse_loss(
                      output, batch["y"]
                  )
                  accelerator.backward(loss)
                  optimizer.step()
  
          model = accelerator.unwrap_model(model).cpu()
          assert torch.allclose(
              old_model.a, model.a
          ), "Did not obtain the same model on CPU or distributed training."
          assert torch.allclose(
              old_model.b, model.b
          ), "Did not obtain the same model on CPU or distributed training."
  
      # IPEX support is only for CPU
      if is_ipex_available():
          print("ipex BF16 training check.")
          AcceleratorState._reset_state()
          accelerator = Accelerator(
              mixed_precision="bf16",
              cpu=True,
              use_seedable_sampler=use_seedable_sampler,
          )
          train_dl = DataLoader(
              train_set,
              batch_size=batch_size,
              shuffle=True,
              generator=generator,
          )
          model = RegressionModel()
          optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
  
          train_dl, model, optimizer = accelerator.prepare(
              train_dl, model, optimizer
          )
          set_seed(42)
          generator.manual_seed(42)
          for _ in range(3):
              for batch in train_dl:
                  model.zero_grad()
                  output = model(batch["x"])
                  loss = torch.nn.functional.mse_loss(
                      output, batch["y"]
                  )
                  accelerator.backward(loss)
                  optimizer.step()
  
          model = accelerator.unwrap_model(model).cpu()
          assert torch.allclose(
              old_model.a, model.a
          ), "Did not obtain the same model on CPU or distributed training."
          assert torch.allclose(
              old_model.b, model.b
          ), "Did not obtain the same model on CPU or distributed training."
  
      # XPU support is only for XPU
      if is_xpu_available():
          print("xpu BF16 training check.")
          AcceleratorState._reset_state()
          accelerator = Accelerator(
              mixed_precision="bf16",
              cpu=False,
              use_seedable_sampler=use_seedable_sampler,
          )
          train_dl = DataLoader(
              train_set,
              batch_size=batch_size,
              shuffle=True,
              generator=generator,
          )
          model = RegressionModel()
          optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
  
          train_dl, model, optimizer = accelerator.prepare(
              train_dl, model, optimizer
          )
          set_seed(42)
          generator.manual_seed(42)
          for _ in range(3):
              for batch in train_dl:
                  model.zero_grad()
                  output = model(batch["x"])
                  loss = torch.nn.functional.mse_loss(
                      output, batch["y"]
                  )
                  accelerator.backward(loss)
                  optimizer.step()
  
          model = accelerator.unwrap_model(model).cpu()
          assert torch.allclose(
              old_model.a, model.a
          ), "Did not obtain the same model on XPU or distributed training."
          assert torch.allclose(
              old_model.b, model.b
          ), "Did not obtain the same model on XPU or distributed training."
  
imports: import contextlib
  import io
  import math
  import time
  from copy import deepcopy
  from pathlib import Path
  import numpy
  import torch
  from torch.utils.data import DataLoader, Dataset
  from accelerate import Accelerator
  from accelerate.data_loader import SeedableRandomSampler, prepare_data_loader
  from accelerate.state import AcceleratorState
  from accelerate.test_utils import RegressionDataset, are_the_same_tensors
  from accelerate.utils import DistributedType, gather, is_bf16_available, is_ipex_available, is_npu_available, is_xpu_available, set_seed, synchronize_rng_states
label_desc: Use image node_id 12 for calling a global function with example usage: training_check(use_seedable_sampler) without return types