class_docstr: Wraps a NumPy array for storage in an object-based checkpoint.
docstr: Specify a NumPy array to wrap.
  
  Args:
    array: The NumPy array to save and restore (may be overwritten).
docstr_tok: ["Specify","a","NumPy","array","to","wrap",".","Args",":","array",":","The","NumPy","array","to","save","and","restore","(","may","be","overwritten",")","."]
code: def __init__(self, array):
      """Specify a NumPy array to wrap.
  
      Args:
        array: The NumPy array to save and restore (may be overwritten).
      """
      self.array = array
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import io
  import numpy
  import tensorflow
  from tf_agents.specs import array_spec
  from tensorflow.python.trackable import base
  from tensorflow.python.trackable import data_structures
label_desc: Use image node_id 1 to create a new _NumpyWrapper object from inherited base classes: tf.train.experimental with example: obj = _NumpyWrapper(array)