class_docstr: It is base class for RNN layer classes.
  It contains some common fields and methods for child classes.
class_docstr_tok: ["It","is","base","class","for","RNN","layer","classes",".","It","contains","some","common","fields","and","methods","for","child","classes","."]
docstr: Generate random weigths for the model
docstr_tok: ["Generate","random","weigths","for","the","model"]
code: def gen_rnd_weights(self):
      """
      Generate random weigths for the model
      """
      if self.model is None:
          raise NotImplementedError(
              "self.model must be defined in subclasses!"
          )
      with torch.no_grad():
          for weight_group in self.model.all_weights:
              for weight in weight_group:
                  weight.data = torch.rand(weight.shape)
  
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 3 for calling the RNN_Model obj's underlying member method code with example usage: obj.gen_rnd_weights() without return types