code: def __init__(
      self,
      num_heads: int,
      num_buckets: int = 32,
      num_segment_bucket: int = 32,
      max_distance: int = 128,
      dtype: torch.dtype = torch.half,
      init_mean: float = 0.0,
      init_std: float = 1,
  ) -> None:
      super().__init__()
  
      self.num_heads = num_heads
      self.num_buckets = num_buckets
      self.num_segment_bucket = num_segment_bucket
      self.max_distance = max_distance
  
      self.relative_attention_bias = bmt.DistributedParameter(
          torch.empty(
              num_buckets + num_segment_bucket, num_heads, dtype=dtype
          ),
          init_method=bmt.ParameterInitializer(
              torch.nn.init.normal_, mean=init_mean, std=init_std
          ),
      )
  
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 BucketPositionBias object from inherited base classes: bmt with example: obj = BucketPositionBias(num_heads, num_buckets, num_segment_bucket, max_distance, dtype, init_mean, init_std)