code: def forward(self, q, k, v, mask):
      # size = [B, L, H, E]
      B, L, H, E = q.shape
  
      x = q.permute(0, 2, 3, 1)
      # Compute Fourier coefficients
      x_ft = torch.fft.rfft(x, dim=-1)
      # Perform Fourier neural operations
      out_ft = torch.zeros(
          B, H, E, L // 2 + 1, device=x.device, dtype=torch.cfloat
      )
      for wi, i in enumerate(self.index):
          out_ft[:, :, :, wi] = self.compl_mul1d(
              x_ft[:, :, :, i], self.weights1[:, :, :, wi]
          )
      # Return to time domain
      x = torch.fft.irfft(out_ft, n=x.size(-1))
      return (x, None)
  
imports: import numpy
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from ..common._modules import DataEmbedding
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 3 for calling the FourierBlock obj's underlying member method code with example usage: obj.forward(q, k, v, mask) and returns: x,  None