code: def _update_context(
      self, context_in, V, scores, index, L_Q, attn_mask
  ):
      B, H, L_V, D = V.shape
  
      if self.mask_flag:
          attn_mask = ProbMask(
              B, H, L_Q, index, scores, device=V.device
          )
          scores.masked_fill_(attn_mask.mask, -np.inf)
  
      attn = torch.softmax(scores, dim=-1)  # nn.Softmax(dim=-1)(scores)
  
      context_in[
          torch.arange(B)[:, None, None],
          torch.arange(H)[None, :, None],
          index,
          :,
      ] = torch.matmul(attn, V).type_as(context_in)
      if self.output_attention:
          attns = (
              (torch.ones([B, H, L_V, L_V]) / L_V)
              .type_as(attn)
              .to(attn.device)
          )
          attns[
              torch.arange(B)[:, None, None],
              torch.arange(H)[None, :, None],
              index,
              :,
          ] = attn
          return (context_in, attns)
      else:
          return (context_in, None)
  
imports: import os
  import math
  import numpy
  from math import sqrt
  from scipy.fftpack import next_fast_len
label_desc: Use image node_id 4 for calling the ProbAttention obj's underlying member method code with example usage: obj._update_context(context_in, V, scores, index, L_Q, attn_mask) and returns: context_in,  attns, context_in,  None