class_docstr: An object to be persisted instead of numpy arrays.
  
  This object is used to hack into the pickle machinery and read numpy
  array data from our custom persistence format.
  More precisely, this object is used for:
  * carrying the information of the persisted array: subclass, shape, order,
  dtype. Those ndarray metadata are used to correctly reconstruct the array
  with low level numpy functions.
  * determining if memmap is allowed on the array.
  * reading the array bytes from a file.
  * reading the array using memorymap from a file.
  * writing the array bytes to a file.
  
  Attributes
  ----------
  subclass: numpy.ndarray subclass
      Determine the subclass of the wrapped array.
  shape: numpy.ndarray shape
      Determine the shape of the wrapped array.
  order: {'C', 'F'}
      Determine the order of wrapped array data. 'C' is for C order, 'F' is
      for fortran order.
  dtype: numpy.ndarray dtype
      Determine the data type of the wrapped array.
  allow_mmap: bool
      Determine if memory mapping is allowed on the wrapped array.
      Default: False.
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docstr: Constructor. Store the useful information for later.
docstr_tok: ["Constructor",".","Store","the","useful","information","for","later","."]
code: def __init__(self, subclass, shape, order, dtype, allow_mmap=False):
      """Constructor. Store the useful information for later."""
      self.subclass = subclass
      self.shape = shape
      self.order = order
      self.dtype = dtype
      self.allow_mmap = allow_mmap
  
imports: import pickle
  import os
  import sys
  import warnings
  from .numpy_pickle_utils import _COMPRESSORS
  from .numpy_pickle_utils import BinaryZlibFile
  from .numpy_pickle_utils import Unpickler, Pickler
  from .numpy_pickle_utils import _read_fileobject, _write_fileobject
  from .numpy_pickle_utils import _read_bytes, BUFFER_SIZE
  from .numpy_pickle_compat import load_compatibility
  from .numpy_pickle_compat import NDArrayWrapper
  from .numpy_pickle_compat import ZNDArrayWrapper
  from ._compat import _basestring, PY3_OR_LATER
  from .backports import make_memmap
label_desc: Use image node_id 1 to create a new NumpyArrayWrapper object from inherited base classes: object with example: obj = NumpyArrayWrapper(subclass, shape, order, dtype, allow_mmap)