code: def forward(self, *, token_embedding, token_len):
      for conv in self.convolutions:
          token_embedding = F.dropout(
              F.relu(conv(token_embedding)), 0.5, self.training
          )
  
      token_embedding = token_embedding.transpose(1, 2)
  
      # pytorch tensor are not reversible, hence the conversion
      input_lengths = token_len.cpu().numpy()
  
      token_embedding = torch.nn.utils.rnn.pack_padded_sequence(
          token_embedding,
          input_lengths,
          batch_first=True,
          enforce_sorted=False,
      )
  
      self.lstm.flatten_parameters()
      outputs, _ = self.lstm(token_embedding)
  
      outputs, _ = torch.nn.utils.rnn.pad_packed_sequence(
          outputs, batch_first=True
      )
  
      return outputs
  
imports: import torch
  from torch.autograd import Variable
  from torch.nn import functional
  from nemo.collections.tts.modules.submodules import Attention, ConvNorm, LinearNorm, Prenet
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
  from nemo.core.classes import NeuralModule, typecheck
  from nemo.core.neural_types.elements import EmbeddedTextType, LengthsType, LogitsType, MelSpectrogramType, SequenceToSequenceAlignmentType
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the Encoder obj's underlying member method code with example usage: obj.forward() and returns: outputs