code: def _get_initial_context(self, V, L_Q):
      B, H, L_V, D = V.shape
      if not self.mask_flag:
          # V_sum = V.sum(dim=-2)
          V_sum = V.mean(dim=-2)
          contex = (
              V_sum.unsqueeze(-2)
              .expand(B, H, L_Q, V_sum.shape[-1])
              .clone()
          )
      else:  # use mask
          assert (
              L_Q == L_V
          )  # requires that L_Q == L_V, i.e. for self-attention only
          contex = V.cumsum(dim=-2)
      return contex
  
imports: import os
  import math
  import numpy
  from math import sqrt
  from scipy.fftpack import next_fast_len
label_desc: Use image node_id 3 for calling the ProbAttention obj's underlying member method code with example usage: obj._get_initial_context(V, L_Q) and returns: contex