code: def forward(self, q, k, v, mask):
      # size = [B, L, H, E]
      B, L, H, E = q.shape
      xq = q.permute(0, 2, 3, 1)  # size = [B, H, E, L]
      xk = k.permute(0, 2, 3, 1)
      # xv = v.permute(0, 2, 3, 1)
  
      # Compute Fourier coefficients
      xq_ft_ = torch.zeros(
          B,
          H,
          E,
          len(self.index_q),
          device=xq.device,
          dtype=torch.cfloat,
      )
      xq_ft = torch.fft.rfft(xq, dim=-1)
      for i, j in enumerate(self.index_q):
          xq_ft_[:, :, :, i] = xq_ft[:, :, :, j]
      xk_ft_ = torch.zeros(
          B,
          H,
          E,
          len(self.index_kv),
          device=xq.device,
          dtype=torch.cfloat,
      )
      xk_ft = torch.fft.rfft(xk, dim=-1)
      for i, j in enumerate(self.index_kv):
          xk_ft_[:, :, :, i] = xk_ft[:, :, :, j]
  
      # Attention mechanism on frequency domain
      xqk_ft = torch.einsum("bhex,bhey->bhxy", xq_ft_, xk_ft_)
      if self.activation == "tanh":
          xqk_ft = xqk_ft.tanh()
      elif self.activation == "softmax":
          xqk_ft = torch.softmax(abs(xqk_ft), dim=-1)
          xqk_ft = torch.complex(xqk_ft, torch.zeros_like(xqk_ft))
      else:
          raise Exception(
              "{} actiation function is not implemented".format(
                  self.activation
              )
          )
      xqkv_ft = torch.einsum("bhxy,bhey->bhex", xqk_ft, xk_ft_)
      xqkvw = torch.einsum("bhex,heox->bhox", xqkv_ft, self.weights1)
      out_ft = torch.zeros(
          B, H, E, L // 2 + 1, device=xq.device, dtype=torch.cfloat
      )
      for i, j in enumerate(self.index_q):
          out_ft[:, :, :, j] = xqkvw[:, :, :, i]
  
      # Return to time domain
      out = torch.fft.irfft(
          out_ft / self.in_channels / self.out_channels, n=xq.size(-1)
      )
      return (out, 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 FourierCrossAttention obj's underlying member method code with example usage: obj.forward(q, k, v, mask) and returns: out,  None