code: def get_config(**kwargs):
      @dataclass
      class Config:
          vocab_size: int  # vocabulary size
          embedding_dim: int  # embedding size
          max_batch_tokens: int  # max batch token numbers
          max_seq_len: int  # max sequence length
          padding_idx: int  # padding token id in vocabulary
          dropout: float  # embedding dropout ration
          fp16: bool  # fp16 presion
          local_rank: int  # rank in local node
          trainable_pos: bool = False  # trainable positional embedding
          no_scale_embedding: bool = False  # scale embedding
          layernorm_embedding: bool = False  # layernorm for embedding
          need_offset: bool = False  # position offset for bart
  
      return Config(**kwargs)
  
file_path: lightseq/lightseq/csrc/pytorch/layer_base.py
imports: import __init__
  from dataclasses import dataclass
  from torch import nn
  from csrc.pytorch.util import MODEL_ARCH, check_config
label_desc: Use image node_id 1 for calling the TransformerEmbeddingLayerBase obj's underlying member method code with example usage: obj.get_config() and returns: Config