code: def __init__(
      self,
      in_channels,
      out_channels,
      seq_len_q,
      seq_len_kv,
      modes=64,
      mode_select_method="random",
      activation="tanh",
      policy=0,
  ):
      super(FourierCrossAttention, self).__init__()
      """
          Fourier Cross Attention layer
          """
      self.activation = activation
      self.in_channels = in_channels
      self.out_channels = out_channels
      # get modes for queries and keys (& values) on frequency domain
      self.index_q = get_frequency_modes(
          seq_len_q, modes=modes, mode_select_method=mode_select_method
      )
      self.index_kv = get_frequency_modes(
          seq_len_kv, modes=modes, mode_select_method=mode_select_method
      )
  
      self.scale = 1 / (in_channels * out_channels)
      self.weights1 = nn.Parameter(
          self.scale
          * torch.rand(
              8,
              in_channels // 8,
              out_channels // 8,
              len(self.index_q),
              dtype=torch.cfloat,
          )
      )
  
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 1 to create a new FourierCrossAttention object from inherited base classes: nn with example: obj = FourierCrossAttention(in_channels, out_channels, seq_len_q, seq_len_kv, modes, mode_select_method, activation, policy)