code: def test_deepspeed_with_bfloat16_precision():
      """Test that the DeepSpeed strategy works with bfloat16 precision."""
  
      class Model(nn.Module):
          def __init__(self):
              super().__init__()
              self.layer = nn.Linear(32, 2)
  
          def forward(self, x):
              assert x.dtype == torch.bfloat16
              return self.layer(x)
  
      fabric = Fabric(
          accelerator="cuda",
          devices=2,
          strategy="deepspeed_stage_3",
          precision="bf16-mixed",
      )
      assert isinstance(fabric._strategy.precision, DeepSpeedPrecision)
      assert fabric._strategy.precision.precision == "bf16-mixed"
      assert fabric._strategy.config["zero_optimization"]["stage"] == 3
      fabric.launch()
  
      with fabric.init_module():
          model = Model()
      optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
      model, optimizer = fabric.setup(model, optimizer)
      assert fabric._strategy.config["bf16"]["enabled"]
      assert model.layer.weight.dtype == torch.bfloat16
  
      batch = torch.rand(2, 32, device=fabric.device)
      assert batch.dtype == torch.float32
      loss = model(batch).sum()
      fabric.backward(loss)
      optimizer.step()
      optimizer.zero_grad()
  
imports: import os
  from copy import deepcopy
  from unittest import mock
  from unittest.mock import ANY
  import pytest
  import torch
  import torch.nn
  import torch.nn.functional
  from lightning.fabric import Fabric
  from lightning.fabric.plugins import DeepSpeedPrecision
  from lightning.fabric.strategies import DeepSpeedStrategy
  from torch.utils.data import DataLoader
  from tests_fabric.helpers.models import RandomDataset, RandomIterableDataset
  from tests_fabric.helpers.runif import RunIf
  from tests_fabric.test_fabric import BoringModel
label_desc: Use image node_id 8 for calling a global function with example usage: test_deepspeed_with_bfloat16_precision() and returns: self