class_docstr: A pickler to persist big data efficiently.
  
  The main features of this object are:
  * persistence of numpy arrays in a single file.
  * optional compression with a special care on avoiding memory copies.
  
  Attributes
  ----------
  fp: file
      File object handle used for serializing the input object.
  protocol: int
      Pickle protocol used. Default is pickle.DEFAULT_PROTOCOL under
      python 3, pickle.HIGHEST_PROTOCOL otherwise.
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docstr: Create and returns a numpy array wrapper from a numpy array.
docstr_tok: ["Create","and","returns","a","numpy","array","wrapper","from","a","numpy","array","."]
code: def _create_array_wrapper(self, array):
      """Create and returns a numpy array wrapper from a numpy array."""
      order = (
          "F"
          if (array.flags.f_contiguous and not array.flags.c_contiguous)
          else "C"
      )
      allow_mmap = not self.buffered and not array.dtype.hasobject
      wrapper = NumpyArrayWrapper(
          type(array),
          array.shape,
          order,
          array.dtype,
          allow_mmap=allow_mmap,
      )
  
      return wrapper
  
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 2 for calling the NumpyPickler obj's underlying member method code with example usage: obj._create_array_wrapper(array) and returns: wrapper