code: def init_hidden(self, batch_size):
      num_bi = 1
      if self.bidirectional:
          num_bi = 2
      h = torch.zeros(
          num_bi * self.num_layers, batch_size, self.hidden_size
      )
      c = torch.zeros(
          num_bi * self.num_layers, batch_size, self.hidden_size
      )
      return h.to(self.device), c.to(self.device)
  
imports: import inspect
  import warnings
  from typing import Optional, Tuple, Dict, Any
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.utils import ModelOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.configuration_utils import PretrainedConfig
  from torch.nn import CrossEntropyLoss
  from .abstract_model import AbstractModel
label_desc: Use image node_id 2 for calling the RNNEncoder obj's underlying member method code with example usage: obj.init_hidden(batch_size) and returns: h,  c