code: def load_batch_encodings(files, sample_length=125):
      """Load a batch of encodings from .npy files.
  
      Args:
        files: A list of filepaths to .npy files
        sample_length: Maximum sample length
  
      Raises:
        ValueError: .npy array has wrong dimensions.
  
      Returns:
        batch: A padded array encodings [batch, length, dims]
      """
      batch = []
      # Load the data
      for f in files:
          data = np.load(f)
          if data.ndim != 2:
              raise ValueError(
                  "Encoding file should have 2 dims "
                  "[time, channels], not {}".format(data.ndim)
              )
          length, channels = data.shape
          # Add padding or crop if not equal to sample length
          if length < sample_length:
              padded = np.zeros([sample_length, channels])
              padded[:length, :] = data
              batch.append(padded)
          else:
              batch.append(data[:sample_length])
      # Return as an numpy array
      batch = np.array(batch)
      return batch
  
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 6 for calling a global function with example usage: load_batch_encodings(files, sample_length) and returns: batch