code: def _shift(self, x, qlen, klen, mask, left=False):
      if qlen > 1:
          zero_pad = torch.zeros(
              (x.size(0), qlen - 1, x.size(2), x.size(3)),
              device=x.device,
              dtype=x.dtype,
          )
      else:
          zero_pad = torch.zeros(0, device=x.device, dtype=x.dtype)
  
      if left:
          mask = mask.flip(1)
          x_padded = torch.cat([zero_pad, x], dim=1).expand(
              qlen, -1, -1, -1
          )
      else:
          x_padded = torch.cat([x, zero_pad], dim=1).expand(
              qlen, -1, -1, -1
          )
  
      x = x_padded.masked_select(mask[:, :, None, None]).view(
          qlen, klen, x.size(2), x.size(3)
      )
  
      return x
  
imports: import sys
  import math
  import functools
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from proj_adaptive_softmax import ProjectedAdaptiveLogSoftmax, Projection
  from log_uniform_sampler import LogUniformSampler, sample_logits
  from fmoe import FMoETransformerMLP
label_desc: Use image node_id 3 for calling the RelMultiHeadAttn obj's underlying member method code with example usage: obj._shift(x, qlen, klen, mask, left) and returns: x