code: def __init__(
      self,
      context_length: int,
      prediction_length: int,
      d_hidden: int,
      m_ffn: int,
      n_head: int,
      n_layers: int,
      n_output: int,
      cardinalities: List[int],
      kernel_sizes: Optional[List[int]],
      dist_enc: Optional[str],
      pre_ln: bool,
      dropout: float,
      temperature: float,
      normalizer_eps: float = 1e-5,
      **kwargs,
  ):
      super().__init__(**kwargs)
      if kernel_sizes is None or len(kernel_sizes) == 0:
          self.kernel_sizes = (1,)
      else:
          self.kernel_sizes = kernel_sizes
      self.context_length = context_length
      self.prediction_length = prediction_length
      self.d_hidden = d_hidden
      assert (n_output % 2 == 1) and (n_output <= 9)
      self.quantiles = sum(
          (
              [i / 10, 1.0 - i / 10]
              for i in range(1, (n_output + 1) // 2)
          ),
          [0.5],
      )
      self.normalizer_eps = normalizer_eps
  
      with self.name_scope():
          self._blocks = []
          for layer in range(n_layers):
              block = SelfAttentionBlock(
                  d_hidden=self.d_hidden,
                  m_ffn=m_ffn,
                  kernel_sizes=self.kernel_sizes,
                  n_head=n_head,
                  dist_enc=dist_enc,
                  pre_ln=pre_ln,
                  dropout=dropout,
                  temperature=temperature,
              )
              self.register_child(block=block, name=f"block_{layer+1}")
              self._blocks.append(block)
  
          self.target_proj = nn.Dense(
              units=self.d_hidden,
              in_units=1,
              use_bias=True,
              flatten=False,
              weight_initializer=init.Xavier(),
              prefix="target_proj_",
          )
          self.covar_proj = nn.Dense(
              units=self.d_hidden,
              use_bias=True,
              flatten=False,
              weight_initializer=init.Xavier(),
              prefix="covar_proj_",
          )
          if cardinalities:
              self.embedder = FeatureEmbedder(
                  cardinalities=cardinalities,
                  embedding_dims=[self.d_hidden] * len(cardinalities),
                  prefix="embedder_",
              )
          self.output = QuantileOutput(quantiles=self.quantiles)
          self.output_proj = self.output.get_quantile_proj()
          self.loss = self.output.get_loss()
  
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 SelfAttentionNetwork object from inherited base classes: HybridBlock with example: obj = SelfAttentionNetwork(context_length, prediction_length, d_hidden, m_ffn, n_head, n_layers, n_output, cardinalities, kernel_sizes, dist_enc, pre_ln, dropout, temperature, normalizer_eps)