class_docstr: An object to be persisted instead of numpy arrays.
  
  The only thing this object does, is to carry the filename in which
  the array has been persisted, and the array subclass.
class_docstr_tok: ["An","object","to","be","persisted","instead","of","numpy","arrays",".","The","only","thing","this","object","does",",","is","to","carry","the","filename","in","which","the","array","has","been","persisted",",","and","the","array","subclass","."]
docstr: Reconstruct the array
docstr_tok: ["Reconstruct","the","array"]
code: def read(self, unpickler):
      "Reconstruct the array"
      filename = os.path.join(unpickler._dirname, self.filename)
      # Load the array from the disk
      if unpickler.np.__version__ >= "1.3":
          array = unpickler.np.load(
              filename, mmap_mode=unpickler.mmap_mode
          )
      else:
          # Numpy does not have mmap_mode before 1.3
          array = unpickler.np.load(filename)
      # Reconstruct subclasses. This does not work with old
      # versions of numpy
      if hasattr(array, "__array_prepare__") and not self.subclass in (
          unpickler.np.ndarray,
          unpickler.np.memmap,
      ):
          # We need to reconstruct another subclass
          new_array = unpickler.np.core.multiarray._reconstruct(
              self.subclass, (0,), "b"
          )
          new_array.__array_prepare__(array)
          array = new_array
      return array
  
imports: import traceback
  import os
  import zlib
  import warnings
  import dill
  import pickle
  from io import BytesIO
label_desc: Use image node_id 2 for calling the NDArrayWrapper obj's underlying member method code with example usage: obj.read(unpickler) and returns: array