class_docstr: Wraps a NumPy array for storage in an object-based checkpoint.
class_docstr_tok: ["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: import io
  import os
  import numpy
  from tensorflow.python.checkpoint import checkpoint
  from tensorflow.python.client import session
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.module import module
  from tensorflow.python.platform import test
  from tensorflow.python.trackable import python_state
label_desc: Use image node_id 1 to create a new _NumpyWrapper object from inherited base classes: python_state with example: obj = _NumpyWrapper(array)