code: def __init__(
      self,
      d_feat=6,
      output_dim=1,
      freq_dim=10,
      hidden_size=64,
      dropout_W=0.0,
      dropout_U=0.0,
      device="cpu",
  ):
      super().__init__()
  
      self.input_dim = d_feat
      self.output_dim = output_dim
      self.freq_dim = freq_dim
      self.hidden_dim = hidden_size
      self.device = device
  
      self.W_i = nn.Parameter(
          init.xavier_uniform_(
              torch.empty((self.input_dim, self.hidden_dim))
          )
      )
      self.U_i = nn.Parameter(
          init.orthogonal_(
              torch.empty(self.hidden_dim, self.hidden_dim)
          )
      )
      self.b_i = nn.Parameter(torch.zeros(self.hidden_dim))
  
      self.W_ste = nn.Parameter(
          init.xavier_uniform_(
              torch.empty(self.input_dim, self.hidden_dim)
          )
      )
      self.U_ste = nn.Parameter(
          init.orthogonal_(
              torch.empty(self.hidden_dim, self.hidden_dim)
          )
      )
      self.b_ste = nn.Parameter(torch.ones(self.hidden_dim))
  
      self.W_fre = nn.Parameter(
          init.xavier_uniform_(
              torch.empty(self.input_dim, self.freq_dim)
          )
      )
      self.U_fre = nn.Parameter(
          init.orthogonal_(torch.empty(self.hidden_dim, self.freq_dim))
      )
      self.b_fre = nn.Parameter(torch.ones(self.freq_dim))
  
      self.W_c = nn.Parameter(
          init.xavier_uniform_(
              torch.empty(self.input_dim, self.hidden_dim)
          )
      )
      self.U_c = nn.Parameter(
          init.orthogonal_(
              torch.empty(self.hidden_dim, self.hidden_dim)
          )
      )
      self.b_c = nn.Parameter(torch.zeros(self.hidden_dim))
  
      self.W_o = nn.Parameter(
          init.xavier_uniform_(
              torch.empty(self.input_dim, self.hidden_dim)
          )
      )
      self.U_o = nn.Parameter(
          init.orthogonal_(
              torch.empty(self.hidden_dim, self.hidden_dim)
          )
      )
      self.b_o = nn.Parameter(torch.zeros(self.hidden_dim))
  
      self.U_a = nn.Parameter(
          init.orthogonal_(torch.empty(self.freq_dim, 1))
      )
      self.b_a = nn.Parameter(torch.zeros(self.hidden_dim))
  
      self.W_p = nn.Parameter(
          init.xavier_uniform_(
              torch.empty(self.hidden_dim, self.output_dim)
          )
      )
      self.b_p = nn.Parameter(torch.zeros(self.output_dim))
  
      self.activation = nn.Tanh()
      self.inner_activation = nn.Hardsigmoid()
      self.dropout_W, self.dropout_U = (dropout_W, dropout_U)
      self.fc_out = nn.Linear(self.output_dim, 1)
  
      self.states = []
  
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 1 to create a new SFM_Model object from inherited base classes: nn with example: obj = SFM_Model(d_feat, output_dim, freq_dim, hidden_size, dropout_W, dropout_U, device)