code: def test_merge_fused_params_update(self) -> None:
      # Case fused_params is None, change it to be an empty dict
      # and set cache_precision to be the same as weights_precision
      fused_params = None
      configured_fused_params = merge_fused_params(
          fused_params=fused_params,
          param_fused_params={"learning_rate": 0.0},
      )
      self.assertFalse(configured_fused_params is None)
      self.assertEqual(configured_fused_params, {"learning_rate": 0.0})
  
imports: import itertools
  import math
  import os
  import random
  import unittest
  from typing import cast, List, Optional, Tuple
  import torch
  import torch.distributed
  from hypothesis import given, settings, strategies, Verbosity
  from torchrec.distributed.embedding_sharding import bucketize_kjt_before_all2all
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.test_utils.test_model import TestSparseNN
  from torchrec.distributed.types import BoundsCheckMode, CacheAlgorithm, CacheParams, DataType, ModuleSharder, ParameterSharding
  from torchrec.distributed.utils import add_params_from_parameter_sharding, convert_to_fbgemm_types, get_unsharded_module_names, merge_fused_params
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from torchrec.sparse.test_utils import keyed_jagged_tensor_equals
  from torchrec.test_utils import get_free_port
label_desc: Use image node_id 2 for calling the MergeFusedParamsTest obj's underlying member method code with example usage: obj.test_merge_fused_params_update() without return types