code: def test_deepspeed_custom_precision_params():
      """Test that if the FP16 parameters are set via the DeepSpeedStrategy, the deepspeed config contains these
      changes."""
      strategy = DeepSpeedStrategy(
          loss_scale=10,
          initial_scale_power=11,
          loss_scale_window=12,
          hysteresis=13,
          min_loss_scale=14,
      )
      fabric = Fabric(
          strategy=strategy,
          precision="16-mixed",
          accelerator="cuda",
          devices=1,
      )
      fabric.launch()
      assert fabric._strategy._config_initialized
      assert fabric._strategy.config["fp16"]["loss_scale"] == 10
      assert (
          fabric._strategy.config["fp16"]["initial_scale_power"] == 11
      )
      assert fabric._strategy.config["fp16"]["loss_scale_window"] == 12
      assert fabric._strategy.config["fp16"]["hysteresis"] == 13
      assert fabric._strategy.config["fp16"]["min_loss_scale"] == 14
  
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 4 for calling a global function with example usage: test_deepspeed_custom_precision_params() without return types