code: def forward(self, queries, keys, values, attn_mask):
      B, L_Q, H, D = queries.shape
      _, L_K, _, _ = keys.shape
  
      queries = queries.transpose(2, 1)
      keys = keys.transpose(2, 1)
      values = values.transpose(2, 1)
  
      U_part = (
          self.factor * np.ceil(np.log(L_K)).astype("int").item()
      )  # c*ln(L_k)
      u = (
          self.factor * np.ceil(np.log(L_Q)).astype("int").item()
      )  # c*ln(L_q)
  
      U_part = U_part if U_part < L_K else L_K
      u = u if u < L_Q else L_Q
  
      scores_top, index = self._prob_QK(
          queries, keys, sample_k=U_part, n_top=u
      )
  
      # add scale factor
      scale = self.scale or 1.0 / math.sqrt(D)
      if scale is not None:
          scores_top = scores_top * scale
      # get the context
      context = self._get_initial_context(values, L_Q)
      # update the context with selected top_k queries
      context, attn = self._update_context(
          context, values, scores_top, index, L_Q, attn_mask
      )
  
      return context.contiguous(), attn
  
imports: import math
  import numpy
  from typing import Optional
  import torch
  import torch.nn
  from neuralforecast.common._modules import TransEncoderLayer, TransEncoder, TransDecoderLayer, TransDecoder, DataEmbedding, AttentionLayer
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 5 for calling the ProbAttention obj's underlying member method code with example usage: obj.forward(queries, keys, values, attn_mask) and returns: context,  attn