docstr: Parameters
  ----------
  x : torch.Tensor
      input data
  
  Returns
  -------
  torch.Tensor
      Updated representations
docstr_tok: ["Parameters","--","--","--","--","--","x",":","torch.Tensor","input","data","Returns","--","--","--","-","torch.Tensor","Updated","representations"]
code: def forward(self, x):
      """
      Parameters
      ----------
      x : torch.Tensor
          input data
  
      Returns
      -------
      torch.Tensor
          Updated representations
      """
  
      # input shape: [batch_size, seq_len*input_dim]
      # output shape: [batch_size, seq_len, input_dim]
      x = (
          x.view(x.shape[0], -1, self.input_dim)
          .permute(0, 2, 1)
          .to(self.device)
      )
      y = self.conv(x)  # [batch_size, output_dim, conved_seq_len]
      y = y.permute(0, 2, 1)  # [batch_size, conved_seq_len, output_dim]
  
      return y
  
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 CNNEncoderBase obj's underlying member method code with example usage: obj.forward(x) and returns: y