cVladu opened a new issue, #11827:
URL: https://github.com/apache/tvm/issues/11827

   I built a simple RNN network in PyTorch and tried to convert it using 
`relay.frontend.from_pytorch` interface
   
   ### Expected behavior
   The network is converted
   
   
   ### Actual behavior
   
   NotImplementedError: The following operators are not implemented: 
['aten::rnn_tanh'] is raised
   
   ### Environment
   
   Operating system:
   Distributor ID: Ubuntu
   Description:    Ubuntu 18.04.6 LTS
   Release:        18.04
   Codename:       bionic
   
   TVM version: 0.9.dev0
   Steps to build the TVM were followed from: 
https://tvm.apache.org/docs/install/from_source.html -- no change to 
config.cmake file
   
   ### Steps to reproduce
   
   ```
   def test_RNN_torch(num_layers: int,
                                       bidirectional: bool,
                                       use_bias: bool,
                                       hidden_size: int,
                                       input_size: int,
                                       seq_len: int,
                                       batch_first: bool,
                                       batch_size: int):
       r''' 
       Args:
           num_layers (int): num_layers to be passed to torch.nn.RNN
           bidirectional (bool): whether to build bidirectional RNN or not
           use_bias (bool): whether to use bias or not
           hidden_size (int): hidden_size of RNN cells
           input_size (int): Input features
           seq_len (int): Timesteps in input data
           batch_first (bool): Whether batch dimension is first or second 
dimension in input tensor
           batch_size (int): Batch size of input. If 0, unbatched input will be 
fed to network
       '''
   
       if batch_first:
           input_shape = (batch_size, seq_len, input_size)
       else:
           input_shape = (seq_len, batch_size, input_size)
       pytorch_net = torch.nn.Sequential(
           torch.nn.RNN(input_size,
                        hidden_size,
                        batch_first=batch_first,
                        num_layers=num_layers,
                        bidirectional=bidirectional,
                        bias=use_bias)
       )
   
       scripted_model = torch.jit.trace(pytorch_net.eval(),
                                        torch.randn(input_shape))
   
       mod, params = relay.frontend.from_pytorch(scripted_model,
                                                 [('input', input_shape)])
       mod = relay.transform.InferType()(mod)
       print(mod.astext())
   
   if __name__ == "__main__":
   
       test_transform_LSTM_3D_to_4D_09(1,
                                       False,
                                       True,
                                       5,
                                       5,
                                       15,
                                       True,
                                       32)
   ```


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