code: def load_nsynth(batch_size=1, sample_length=64000):
      """Load the NSynth autoencoder network.
  
      Args:
        batch_size: Batch size number of observations to process. [1]
        sample_length: Number of samples in the input audio. [64000]
      Returns:
        graph: The network as a dict with input placeholder in {"X"}
      """
      config = Config()
      with tf.device("/gpu:0"):
          x = tf.placeholder(
              tf.float32, shape=[batch_size, sample_length]
          )
          graph = config.build({"wav": x}, is_training=False)
          graph.update({"X": x})
      return graph
  
file_path: magenta/magenta/models/nsynth/wavenet/fastgen.py
imports: from magenta.models.nsynth import utils
  from magenta.models.nsynth.wavenet.h512_bo16 import Config
  from magenta.models.nsynth.wavenet.h512_bo16 import FastGenerationConfig
  import numpy
  from scipy.io import wavfile
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: load_nsynth(batch_size, sample_length) and returns: graph