code: def dft_forward(self, x):
      T = x.size(1)
  
      dft_mat = fft.fft(torch.eye(T))
      i, j = torch.meshgrid(
          torch.arange(self.pred_len + T), torch.arange(T)
      )
      omega = np.exp(2 * math.pi * 1j / T)
      idft_mat = (np.power(omega, i * j) / T).cfloat()
  
      x_freq = torch.einsum("ft,btd->bfd", [dft_mat, x.cfloat()])
  
      if T % 2 == 0:
          x_freq = x_freq[:, self.low_freq : T // 2]
      else:
          x_freq = x_freq[:, self.low_freq : T // 2 + 1]
  
      _, indices = torch.topk(
          x_freq.abs(), self.k, dim=1, largest=True, sorted=True
      )
      indices = indices + self.low_freq
      indices = torch.cat([indices, -indices], dim=1)
  
      dft_mat = repeat(
          dft_mat, "f t -> b f t d", b=x.shape[0], d=x.shape[-1]
      )
      idft_mat = repeat(
          idft_mat, "t f -> b t f d", b=x.shape[0], d=x.shape[-1]
      )
  
      mesh_a, mesh_b = torch.meshgrid(
          torch.arange(x.size(0)), torch.arange(x.size(2))
      )
  
      dft_mask = torch.zeros_like(dft_mat)
      dft_mask[mesh_a, indices, :, mesh_b] = 1
      dft_mat = dft_mat * dft_mask
  
      idft_mask = torch.zeros_like(idft_mat)
      idft_mask[mesh_a, :, indices, mesh_b] = 1
      idft_mat = idft_mat * idft_mask
  
      attn = torch.einsum("bofd,bftd->botd", [idft_mat, dft_mat]).real
      return torch.einsum("botd,btd->bod", [attn, x]), rearrange(
          attn, "b o t d -> b d o t"
      )
  
imports: import numpy
  import math
  from math import sqrt
  import os
  from scipy.fftpack import next_fast_len
  from merlion.models.utils.nn_modules.blocks import ExponentialSmoothing, conv1d_fft
label_desc: Use image node_id 5 for calling the FourierLayer obj's underlying member method code with example usage: obj.dft_forward(x) and returns: torch,  rearrange