class_docstr: Encode phonemes/text to vector
class_docstr_tok: ["Encode","phonemes\/text","to","vector"]
code: def __init__(
      self, vocab_size, lstm_dim, dropout_rate, is_training=True
  ):
      super().__init__()
      self.is_training = is_training
      self.embed = hk.Embed(vocab_size, lstm_dim)
      self.conv1 = hk.Conv1D(lstm_dim, 3, padding="SAME")
      self.conv2 = hk.Conv1D(lstm_dim, 3, padding="SAME")
      self.conv3 = hk.Conv1D(lstm_dim, 3, padding="SAME")
      self.bn1 = hk.BatchNorm(True, True, 0.9)
      self.bn2 = hk.BatchNorm(True, True, 0.9)
      self.bn3 = hk.BatchNorm(True, True, 0.9)
      self.lstm_fwd = hk.LSTM(lstm_dim)
      self.lstm_bwd = hk.ResetCore(hk.LSTM(lstm_dim))
      self.dropout_rate = dropout_rate
  
imports: import haiku
  import jax
  import jax.numpy
  from jax.numpy import ndarray
  from .config import FLAGS, AcousticInput, DurationInput
label_desc: Use image node_id 1 to create a new TokenEncoder object from inherited base classes: hk with example: obj = TokenEncoder(vocab_size, lstm_dim, dropout_rate, is_training)