class_docstr: Predict melspectrogram from aligned phonemes
class_docstr_tok: ["Predict","melspectrogram","from","aligned","phonemes"]
code: def inference(self, tokens, durations, n_frames):
      B, L = tokens.shape
      lengths = jnp.array([L], dtype=jnp.int32)
      x = self.encoder(tokens, lengths)
      x = self.upsample(x, durations, n_frames)
  
      def loop_fn(inputs, state):
          cond = inputs
          prev_mel, hxcx = state
          prev_mel = self.prenet(prev_mel)
          x = jnp.concatenate((cond, prev_mel), axis=-1)
          x, new_hxcx = self.decoder(x, hxcx)
          x = self.projection(x)
          return x, (x, new_hxcx)
  
      state = (
          jnp.zeros((B, FLAGS.mel_dim), dtype=jnp.float32),
          self.decoder.initial_state(B),
      )
      x, _ = hk.dynamic_unroll(loop_fn, x, state, time_major=False)
      residual = self.postnet(x)
      return x + residual
  
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 5 for calling the AcousticModel obj's underlying member method code with example usage: obj.inference(tokens, durations, n_frames) and returns: unknown, x,  x,  new_hxcx