code: def test_sharding_fp_ebc_from_meta(
      self, use_fp_collection: bool, backend: str
  ) -> None:
      (
          embedding_bag_config,
          kjt_input_per_rank,
      ) = get_configs_and_kjt_inputs()
      self._run_multi_process_test(
          callable=_test_sharding_from_meta,
          world_size=2,
          tables=embedding_bag_config,
          sharder=FeatureProcessedEmbeddingBagCollectionSharder(),
          backend=backend,
          use_fp_collection=use_fp_collection,
      )
  
imports: import copy
  import unittest
  from operator import xor
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from hypothesis import given, settings, strategies, Verbosity
  from torch.distributed.optim import _apply_optimizer_in_backward
  from torchrec import distributed
  from torchrec.distributed.fp_embeddingbag import FeatureProcessedEmbeddingBagCollectionSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.shard import _shard_modules
  from torchrec.distributed.sharding_plan import column_wise, construct_module_sharding_plan, data_parallel, table_wise
  from torchrec.distributed.test_utils.multi_process import MultiProcessContext, MultiProcessTestBase
  from torchrec.distributed.test_utils.test_sharding import copy_state_dict
  from torchrec.distributed.tests.test_fp_embeddingbag_utils import create_module_and_freeze
  from torchrec.distributed.types import ModuleSharder, ShardingEnv, ShardingPlan
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from torchrec.test_utils import skip_if_asan_class
label_desc: Use image node_id 2 for calling the ShardedEmbeddingBagCollectionParallelTest obj's underlying member method code with example usage: obj.test_sharding_fp_ebc_from_meta(use_fp_collection, backend) without return types