code: def forward(self, input):
      input = input.reshape(len(input), self.input_dim, -1)  # [N, F, T]
      input = input.permute(0, 2, 1)  # [N, T, F]
      time_step = input.shape[1]
  
      for ts in range(time_step):
          x = input[:, ts, :]
          if len(self.states) == 0:  # hasn't initialized yet
              self.init_states(x)
          self.get_constants(x)
          p_tm1 = self.states[0]  # noqa: F841
          h_tm1 = self.states[1]
          S_re_tm1 = self.states[2]
          S_im_tm1 = self.states[3]
          time_tm1 = self.states[4]
          B_U = self.states[5]
          B_W = self.states[6]
          frequency = self.states[7]
  
          x_i = torch.matmul(x * B_W[0], self.W_i) + self.b_i
          x_ste = torch.matmul(x * B_W[0], self.W_ste) + self.b_ste
          x_fre = torch.matmul(x * B_W[0], self.W_fre) + self.b_fre
          x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c
          x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
  
          i = self.inner_activation(
              x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)
          )
          ste = self.inner_activation(
              x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste)
          )
          fre = self.inner_activation(
              x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre)
          )
  
          ste = torch.reshape(ste, (-1, self.hidden_dim, 1))
          fre = torch.reshape(fre, (-1, 1, self.freq_dim))
  
          f = ste * fre
  
          c = i * self.activation(
              x_c + torch.matmul(h_tm1 * B_U[0], self.U_c)
          )
  
          time = time_tm1 + 1
  
          omega = torch.tensor(2 * np.pi) * time * frequency
  
          re = torch.cos(omega)
          im = torch.sin(omega)
  
          c = torch.reshape(c, (-1, self.hidden_dim, 1))
  
          S_re = f * S_re_tm1 + c * re
          S_im = f * S_im_tm1 + c * im
  
          A = torch.square(S_re) + torch.square(S_im)
  
          A = torch.reshape(A, (-1, self.freq_dim)).float()
          A_a = torch.matmul(A * B_U[0], self.U_a)
          A_a = torch.reshape(A_a, (-1, self.hidden_dim))
          a = self.activation(A_a + self.b_a)
  
          o = self.inner_activation(
              x_o + torch.matmul(h_tm1 * B_U[0], self.U_o)
          )
  
          h = o * a
          p = torch.matmul(h, self.W_p) + self.b_p
  
          self.states = [p, h, S_re, S_im, time, None, None, None]
      self.states = []
      return self.fc_out(p).squeeze()
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.nn.init
  import torch.optim
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 2 for calling the SFM_Model obj's underlying member method code with example usage: obj.forward(input) and returns: self