code: def __init__(
      self,
      d_hidden: int,
      m_ffn: int,
      n_head: int,
      kernel_sizes: List[int],
      dist_enc: Optional[str],
      pre_ln: bool,
      dropout: float,
      temperature: float,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.d_hidden = d_hidden
      self.m_ffn = m_ffn
      self.pre_ln = pre_ln
  
      with self.name_scope():
          self.attention = SelfAttention(
              d_hidden=d_hidden,
              kernel_sizes=kernel_sizes,
              n_head=n_head,
              bias=True,
              bidirectional=False,
              dist_enc=dist_enc,
              share_values=False,
              dropout=dropout,
              temperature=temperature,
              prefix="attn_",
          )
          self.lnorm = nn.LayerNorm(axis=-1)
          self.dropout = nn.Dropout(dropout)
          self.ffn = PosFFN(
              d_model=d_hidden,
              d_hidden=d_hidden * m_ffn,
              pre_ln=pre_ln,
              dropout=dropout,
              prefix="ffn_",
          )
  
imports: from typing import List, Optional, Tuple
  from mxnet import init
  from mxnet.gluon import HybridBlock, nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.quantile_output import QuantileOutput
  from gluonts.mx.util import weighted_average
  from ._layers import PosFFN, SelfAttention
label_desc: Use image node_id 1 to create a new SelfAttentionBlock object from inherited base classes: HybridBlock with example: obj = SelfAttentionBlock(d_hidden, m_ffn, n_head, kernel_sizes, dist_enc, pre_ln, dropout, temperature)