class_docstr: It is base class for RNN layer classes.
  It contains some common fields and methods for child classes.
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docstr: Computes the output tensor after input inference along RNN layer.
  
  :param input: batch of data as a tensor of shape (seqs_length, batch_size, feature_size) or (batch_size, seqs_length, feature_size) if self.batch_first = True
  :param hidden_init: initial hidden state(s) of the RNN as a tensor(s) of shape (num_layers, batch_size, hidden_size). Will default to a tensor of zeros if None.
  :return: the output tensor of shape (batch_size, hidden_size)
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code: def forward(self, input, hidden_init=None):
      """
      Computes the output tensor after input inference along RNN layer.
  
      :param input: batch of data as a tensor of shape (seqs_length, batch_size, feature_size) or (batch_size, seqs_length, feature_size) if self.batch_first = True
      :param hidden_init: initial hidden state(s) of the RNN as a tensor(s) of shape (num_layers, batch_size, hidden_size). Will default to a tensor of zeros if None.
      :return: the output tensor of shape (batch_size, hidden_size)
      """
      if self.model is None:
          raise NotImplementedError(
              "self.model must be defined in subclasses!"
          )
      out, _ = self.model(input, hidden_init)
  
      return out
  
imports: import torch
  import tvm
  import tvm.testing
  import onnx
  import io
  import sys
  from tvm import relay
  from tvm.contrib import graph_executor
  from torch import nn
label_desc: Use image node_id 2 for calling the RNN_Model obj's underlying member method code with example usage: obj.forward(input, hidden_init) and returns: out