code: def make_sequence_example(inputs, labels):
      """Returns a SequenceExample for the given inputs and labels.
  
      Args:
        inputs: A list of input vectors. Each input vector is a list of floats.
        labels: A list of ints.
  
      Returns:
        A tf.train.SequenceExample containing inputs and labels.
      """
      input_features = [
          tf.train.Feature(float_list=tf.train.FloatList(value=input_))
          for input_ in inputs
      ]
      label_features = []
      for label in labels:
          if isinstance(label, numbers.Number):
              label = [label]
          label_features.append(
              tf.train.Feature(
                  int64_list=tf.train.Int64List(value=label)
              )
          )
      feature_list = {
          "inputs": tf.train.FeatureList(feature=input_features),
          "labels": tf.train.FeatureList(feature=label_features),
      }
      feature_lists = tf.train.FeatureLists(feature_list=feature_list)
      return tf.train.SequenceExample(feature_lists=feature_lists)
  
file_path: magenta/magenta/pipelines/pipelines_common.py
imports: import numbers
  import random
  from magenta.pipelines import pipeline
  from magenta.pipelines import statistics
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: make_sequence_example(inputs, labels) and returns: tf