docstr: Build K parallel RNNs
  
  Parameters
  ----------
  input_dim : int
      The input dimension
  output_dim : int
      The output dimension
  dup_num : int
      The number of parallel RNNs
  rnn_layers: int
      The number of RNN layers
docstr_tok: ["Build","K","parallel","RNNs","Parameters","--","--","--","--","--","input_dim",":","int","The","input","dimension","output_dim",":","int","The","output","dimension","dup_num",":","int","The","number","of","parallel","RNNs","rnn_layers",":","int","The","number","of","RNN","layers"]
code: def __init__(
      self, input_dim, output_dim, dup_num, rnn_layers, dropout, device
  ):
      """Build K parallel RNNs
  
      Parameters
      ----------
      input_dim : int
          The input dimension
      output_dim : int
          The output dimension
      dup_num : int
          The number of parallel RNNs
      rnn_layers: int
          The number of RNN layers
      """
      super().__init__()
  
      self.input_dim = input_dim
      self.output_dim = output_dim
      self.dup_num = dup_num
      self.rnn_layers = rnn_layers
      self.dropout = dropout
      self.device = device
  
      self.rnn_modules = nn.ModuleList()
      for _ in range(dup_num):
          self.rnn_modules.append(
              nn.GRU(
                  input_dim,
                  output_dim,
                  num_layers=self.rnn_layers,
                  dropout=dropout,
              )
          )
  
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 1 to create a new KRNNEncoderBase object from inherited base classes: nn with example: obj = KRNNEncoderBase(input_dim, output_dim, dup_num, rnn_layers, dropout, device)