code: def convert_optimizer_type_and_kwargs(
      optimizer_type: Type[torch.optim.Optimizer],
      optimizer_kwargs: Dict[str, Any],
  ) -> Optional[Tuple[EmbOptimType, Dict[str, Any]]]:
      optimizer_kwargs = copy.deepcopy(optimizer_kwargs)
      if "lr" in optimizer_kwargs:
          optimizer_kwargs["learning_rate"] = optimizer_kwargs["lr"]
          optimizer_kwargs.pop("lr")
  
      if optimizer_type == torch.optim.SGD:
          return (
              EmbOptimType.EXACT_SGD,
              optimizer_kwargs,
          )
      elif optimizer_type == torch.optim.Adagrad:
          return (EmbOptimType.EXACT_ADAGRAD, optimizer_kwargs)
      elif optimizer_type == trec_optim.RowWiseAdagrad:
          return (EmbOptimType.EXACT_ROWWISE_ADAGRAD, optimizer_kwargs)
      elif optimizer_type == torch.optim.Adam:
          return (EmbOptimType.ADAM, optimizer_kwargs)
  
      return None
  
imports: import copy
  import itertools
  from collections import defaultdict
  from typing import Any, cast, Dict, Iterator, List, Optional, Set, Tuple, Type
  import torch
  import torch.nn
  import torchrec.optim
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from fbgemm_gpu.split_table_batched_embeddings_ops_training import ComputeDevice, EmbeddingLocation, SplitTableBatchedEmbeddingBagsCodegen
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, DataType, EmbeddingBagConfig, EmbeddingConfig, pooling_type_to_pooling_mode, PoolingType
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingBagCollectionInterface, EmbeddingCollection, EmbeddingCollectionInterface, get_embedding_names_by_table
  from torchrec.optim.fused import FusedOptimizer, FusedOptimizerModule
  from torchrec.optim.keyed import CombinedOptimizer, KeyedOptimizer
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 1 for calling a global function with example usage: convert_optimizer_type_and_kwargs(optimizer_type, optimizer_kwargs) and returns: None, EmbOptimType,  optimizer_kwargs, EmbOptimType,  optimizer_kwargs, EmbOptimType,  optimizer_kwargs, EmbOptimType,  optimizer_kwargs