code: def init_states(self, x):
      reducer_f = torch.zeros((self.hidden_dim, self.freq_dim)).to(
          self.device
      )
      reducer_p = torch.zeros((self.hidden_dim, self.output_dim)).to(
          self.device
      )
  
      init_state_h = torch.zeros(self.hidden_dim).to(self.device)
      init_state_p = torch.matmul(init_state_h, reducer_p)
  
      init_state = torch.zeros_like(init_state_h).to(self.device)
      init_freq = torch.matmul(init_state_h, reducer_f)
  
      init_state = torch.reshape(init_state, (-1, self.hidden_dim, 1))
      init_freq = torch.reshape(init_freq, (-1, 1, self.freq_dim))
  
      init_state_S_re = init_state * init_freq
      init_state_S_im = init_state * init_freq
  
      init_state_time = torch.tensor(0).to(self.device)
  
      self.states = [
          init_state_p,
          init_state_h,
          init_state_S_re,
          init_state_S_im,
          init_state_time,
          None,
          None,
          None,
      ]
  
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 3 for calling the SFM_Model obj's underlying member method code with example usage: obj.init_states(x) without return types