code: def hybrid_forward(
      self,
      F,
      x: Tensor,
      mask: Tensor,
  ) -> Tensor:
      skip = x
      if self.pre_ln:
          x = self.lnorm(x)
      x = self.attention(x, mask)
      x = x + skip
      if not self.pre_ln:
          x = self.lnorm(x)
      x = self.ffn(x)
      return x
  
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 2 for calling the SelfAttentionBlock obj's underlying member method code with example usage: obj.hybrid_forward(F, x, mask) and returns: x