code: def __init__(
      self,
      num_heads: int,
      num_segments: int = 1,
      num_buckets: int = 32,
      max_distance: int = 128,
      bidirectional: bool = False,
      dtype: torch.dtype = torch.half,
      init_mean: float = 0.0,
      init_std: float = 1,
  ):
      super().__init__()
  
      self.num_heads = num_heads
      self.num_buckets = num_buckets
      self.max_distance = max_distance
      self.bidirectional = bidirectional
      self.num_segments = num_segments
  
      self.relative_attention_bias = bmt.DistributedParameter(
          torch.empty(
              num_segments * num_segments + num_buckets,
              num_heads,
              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/position_embedding.py
imports: import math
  from typing import Union
  import torch
  import bmtrain
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new SegmentPositionEmbedding object from inherited base classes: bmt with example: obj = SegmentPositionEmbedding(num_heads, num_segments, num_buckets, max_distance, bidirectional, dtype, init_mean, init_std)