code: def _apply_mask(
      self, F, score: Tensor, key_mask: Optional[Tensor]
  ) -> Tensor:
      k_idx = F.contrib.arange_like(score, axis=-1)
      k_idx = (
          k_idx.expand_dims(axis=0)
          .expand_dims(axis=0)
          .expand_dims(axis=0)
      )
      q_idx = (
          F.contrib.arange_like(score, axis=-2) + self.context_length
      )
      q_idx = (
          q_idx.expand_dims(axis=-1)
          .expand_dims(axis=0)
          .expand_dims(axis=0)
      )
      unidir_mask = F.broadcast_lesser_equal(k_idx, q_idx)
      unidir_mask = F.broadcast_like(unidir_mask, score)
      score = F.where(unidir_mask, score, F.ones_like(score) * -1e9)
      if key_mask is not None:
          key_mask = key_mask.expand_dims(axis=1)  # head
          key_mask = key_mask.expand_dims(axis=2)  # query
          key_mask = F.broadcast_like(key_mask, score)
          score = F.where(key_mask, score, F.ones_like(score) * -1e9)
      return score
  
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 5 for calling the SelfAttention obj's underlying member method code with example usage: obj._apply_mask(F, score, key_mask) and returns: score