code: def __init__(
      self,
      context_length: int,
      prediction_length: int,
      d_hidden: int,
      n_head: int = 1,
      bias: bool = True,
      share_values: bool = False,
      dropout: float = 0.0,
      temperature: float = 1.0,
      **kwargs,
  ):
      super().__init__(**kwargs)
      if d_hidden % n_head != 0:
          raise ValueError(
              f"hidden dim {d_hidden} cannot be split into {n_head} heads."
          )
      self.context_length = context_length
      self.prediction_length = prediction_length
      self.d_hidden = d_hidden
      self.n_head = n_head
      self.d_head = d_hidden // n_head
      self.bias = bias
      self.share_values = share_values
      self.temperature = temperature
  
      with self.name_scope():
          self.dropout = nn.Dropout(dropout)
          self.q_proj = nn.Dense(
              units=self.d_hidden,
              in_units=self.d_hidden,
              use_bias=self.bias,
              flatten=False,
              weight_initializer=init.Xavier(),
              prefix="q_proj_",
          )
          self.k_proj = nn.Dense(
              units=self.d_hidden,
              in_units=self.d_hidden,
              use_bias=self.bias,
              flatten=False,
              weight_initializer=init.Xavier(),
              prefix="k_proj_",
          )
          self.v_proj = nn.Dense(
              units=self.d_head if self.share_values else self.d_hidden,
              in_units=self.d_hidden,
              use_bias=self.bias,
              flatten=False,
              weight_initializer=init.Xavier(),
              prefix="v_proj_",
          )
          self.out_proj = nn.Dense(
              units=self.d_hidden,
              in_units=self.d_hidden,
              use_bias=self.bias,
              flatten=False,
              weight_initializer=init.Xavier(),
              prefix="out_proj_",
          )
  
imports: import math
  from typing import List, Optional, Tuple
  from mxnet import init
  from mxnet.gluon import HybridBlock, nn, rnn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 1 to create a new SelfAttention object from inherited base classes: HybridBlock with example: obj = SelfAttention(context_length, prediction_length, d_hidden, n_head, bias, share_values, dropout, temperature)