code: def acoustic_model(inputs, hparams, lstm_units, lengths):
      """Acoustic model that handles all specs for a sequence in one window."""
      conv_output = conv_net(inputs, hparams)
  
      if lstm_units:
          return lstm_layer(
              conv_output,
              lstm_units,
              lengths=lengths if hparams.use_lengths else None,
              stack_size=hparams.acoustic_rnn_stack_size,
              use_cudnn=hparams.use_cudnn,
              bidirectional=hparams.bidirectional,
          )
  
      else:
          return conv_output
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  from magenta.common import flatten_maybe_padded_sequences
  from magenta.common import tf_utils
  from magenta.contrib import cudnn_rnn
  from magenta.contrib import rnn
  from magenta.contrib import training
  from magenta.models.onsets_frames_transcription import constants
  from magenta.models.onsets_frames_transcription import infer_util
  from magenta.models.onsets_frames_transcription import metrics
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tf_slim
label_desc: Use image node_id 3 for calling a global function with example usage: acoustic_model(inputs, hparams, lstm_units, lengths) and returns: lstm_layer, conv_output