code: def __init__(
      self,
      vocab_size: int,
      embedding_size: int,
      dtype: torch.dtype = torch.half,
      init_mean: float = 0.0,
      init_std: float = 1,
  ):
      super().__init__()
  
      self.dim_model = embedding_size
      self.weight = bmt.DistributedParameter(
          torch.empty(vocab_size, embedding_size, dtype=dtype),
          init_method=bmt.ParameterInitializer(
              torch.nn.init.normal_, mean=init_mean, std=init_std
          ),
      )
  
file_path: DeepKE/example/llm/CPM-Bee/src/cpm_live/layers/embedding.py
imports: from typing import Optional
  import torch
  import bmtrain
  import math
  import torch.nn.functional
  from .position_embedding import RotaryEmbedding
label_desc: Use image node_id 1 to create a new Embedding object from inherited base classes: bmt with example: obj = Embedding(vocab_size, embedding_size, dtype, init_mean, init_std)