class_docstr: A wrapper around a pretrained CREPE model, for pitch prediction.
  
  Enables predicting pitch and confidence entirely in TF for running in batch
  on accelerators. For [full,large,small,tiny] crepe models, reads h5 models
  from installed pip package. Other saved models
class_docstr_tok: ["A","wrapper","around","a","pretrained","CREPE","model",",","for","pitch","prediction",".","Enables","predicting","pitch","and","confidence","entirely","in","TF","for","running","in","batch","on","accelerators",".","For","[","full",",","large",",","small",",","tiny","]","crepe","models",",","reads","h5","models","from","installed","pip","package",".","Other","saved","models"]
docstr: Normalize each frame -- this is expected by the model.
docstr_tok: ["Normalize","each","frame","--","this","is","expected","by","the","model","."]
code: def normalize_frames(self, frames):
      """Normalize each frame -- this is expected by the model."""
      mu, var = tf.nn.moments(frames, axes=[-1])
      std = tf.where(tf.abs(var) > 0, tf.sqrt(var), 1e-8)
      frames -= mu[:, None]
      frames /= std[:, None]
      return frames
  
imports: import crepe
  from ddsp import core
  from ddsp.core import safe_log
  from ddsp.core import tf_float32
  import gin
  import librosa
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
label_desc: Use image node_id 4 for calling the PretrainedCREPE obj's underlying member method code with example usage: obj.normalize_frames(frames) and returns: frames