docstr: Parameters
  ----------
  x : torch.Tensor
      Input data
  n_id : torch.Tensor
      Node indices
  
  Returns
  -------
  torch.Tensor
      Updated representations
docstr_tok: ["Parameters","--","--","--","--","--","x",":","torch.Tensor","Input","data","n_id",":","torch.Tensor","Node","indices","Returns","--","--","--","-","torch.Tensor","Updated","representations"]
code: def forward(self, x):
      """
      Parameters
      ----------
      x : torch.Tensor
          Input data
      n_id : torch.Tensor
          Node indices
  
      Returns
      -------
      torch.Tensor
          Updated representations
      """
  
      # input shape: [batch_size, seq_len, input_dim]
      # output shape: [batch_size, seq_len, output_dim]
      # [seq_len, batch_size, input_dim]
      batch_size, seq_len, input_dim = x.shape
      x = x.permute(1, 0, 2).to(self.device)
  
      hids = []
      for rnn in self.rnn_modules:
          h, _ = rnn(x)  # [seq_len, batch_size, output_dim]
          hids.append(h)
      # [seq_len, batch_size, output_dim, num_dups]
      hids = torch.stack(hids, dim=-1)
      hids = hids.view(
          seq_len, batch_size, self.output_dim, self.dup_num
      )
      hids = hids.mean(dim=3)
      hids = hids.permute(1, 0, 2)
  
      return hids
  
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.optim
  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 KRNNEncoderBase obj's underlying member method code with example usage: obj.forward(x) and returns: hids