code: def __init__(
      self,
      n_head=8,
      d_model=512,
      d_k=64,
      d_v=64,
      dropout=0.1,
      qkv_bias=False,
  ):
      super().__init__()
      self.n_head = n_head
      self.d_k = d_k
      self.d_v = d_v
  
      self.dim_k = n_head * d_k
      self.dim_v = n_head * d_v
  
      self.linear_q = nn.Linear(
          self.dim_k, self.dim_k, bias_attr=qkv_bias
      )
      self.linear_k = nn.Linear(
          self.dim_k, self.dim_k, bias_attr=qkv_bias
      )
      self.linear_v = nn.Linear(
          self.dim_v, self.dim_v, bias_attr=qkv_bias
      )
  
      self.attention = ScaledDotProductAttention(d_k**0.5, dropout)
  
      self.fc = nn.Linear(self.dim_v, d_model, bias_attr=qkv_bias)
      self.proj_drop = nn.Dropout(dropout)
  
imports: import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr, reshape, transpose
  from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
  from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
  from paddle.nn.initializer import KaimingNormal, Uniform, Constant
label_desc: Use image node_id 1 to create a new MultiHeadAttention object from inherited base classes: nn with example: obj = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout, qkv_bias)