class_docstr: Create a memory-map to an array stored in a *binary* file on disk.
  
  Memory-mapped files are used for accessing small segments of large files
  on disk, without reading the entire file into memory.  NumPy's
  memmap's are array-like objects.  This differs from Python's ``mmap``
  module, which uses file-like objects.
  
  This subclass of ndarray has some unpleasant interactions with
  some operations, because it doesn't quite fit properly as a subclass.
  An alternative to using this subclass is to create the ``mmap``
  object yourself, then create an ndarray with ndarray.__new__ directly,
  passing the object created in its 'buffer=' parameter.
  
  This class may at some point be turned into a factory function
  which returns a view into an mmap buffer.
  
  Delete the memmap instance to close the memmap file.
  
  
  Parameters
  ----------
  filename : str, file-like object, or pathlib.Path instance
      The file name or file object to be used as the array data buffer.
  dtype : data-type, optional
      The data-type used to interpret the file contents.
      Default is `uint8`.
  mode : {'r+', 'r', 'w+', 'c'}, optional
      The file is opened in this mode:
  
      +------+-------------------------------------------------------------+
      | 'r'  | Open existing file for reading only.                        |
      +------+-------------------------------------------------------------+
      | 'r+' | Open existing file for reading and writing.                 |
      +------+-------------------------------------------------------------+
      | 'w+' | Create or overwrite existing file for reading and writing.  |
      +------+-------------------------------------------------------------+
      | 'c'  | Copy-on-write: assignments affect data in memory, but       |
      |      | changes are not saved to disk.  The file on disk is         |
      |      | read-only.                                                  |
      +------+-------------------------------------------------------------+
  
      Default is 'r+'.
  offset : int, optional
      In the file, array data starts at this offset. Since `offset` is
      measured in bytes, it should normally be a multiple of the byte-size
      of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of
      file are valid; The file will be extended to accommodate the
      additional data. By default, ``memmap`` will start at the beginning of
      the file, even if ``filename`` is a file pointer ``fp`` and
      ``fp.tell() != 0``.
  shape : tuple, optional
      The desired shape of the array. If ``mode == 'r'`` and the number
      of remaining bytes after `offset` is not a multiple of the byte-size
      of `dtype`, you must specify `shape`. By default, the returned array
      will be 1-D with the number of elements determined by file size
      and data-type.
  order : {'C', 'F'}, optional
      Specify the order of the ndarray memory layout:
      :term:`row-major`, C-style or :term:`column-major`,
      Fortran-style.  This only has an effect if the shape is
      greater than 1-D.  The default order is 'C'.
  
  Attributes
  ----------
  filename : str or pathlib.Path instance
      Path to the mapped file.
  offset : int
      Offset position in the file.
  mode : str
      File mode.
  
  Methods
  -------
  flush
      Flush any changes in memory to file on disk.
      When you delete a memmap object, flush is called first to write
      changes to disk before removing the object.
  
  
  See also
  --------
  lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
  
  Notes
  -----
  The memmap object can be used anywhere an ndarray is accepted.
  Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns
  ``True``.
  
  Memory-mapped files cannot be larger than 2GB on 32-bit systems.
  
  When a memmap causes a file to be created or extended beyond its
  current size in the filesystem, the contents of the new part are
  unspecified. On systems with POSIX filesystem semantics, the extended
  part will be filled with zero bytes.
  
  Examples
  --------
  >>> data = np.arange(12, dtype='float32')
  >>> data.resize((3,4))
  
  This example uses a temporary file so that doctest doesn't write
  files to your directory. You would use a 'normal' filename.
  
  >>> from tempfile import mkdtemp
  >>> import os.path as path
  >>> filename = path.join(mkdtemp(), 'newfile.dat')
  
  Create a memmap with dtype and shape that matches our data:
  
  >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))
  >>> fp
  memmap([[ 0.,  0.,  0.,  0.],
          [ 0.,  0.,  0.,  0.],
          [ 0.,  0.,  0.,  0.]], dtype=float32)
  
  Write data to memmap array:
  
  >>> fp[:] = data[:]
  >>> fp
  memmap([[  0.,   1.,   2.,   3.],
          [  4.,   5.,   6.,   7.],
          [  8.,   9.,  10.,  11.]], dtype=float32)
  
  >>> fp.filename == path.abspath(filename)
  True
  
  Deletion flushes memory changes to disk before removing the object:
  
  >>> del fp
  
  Load the memmap and verify data was stored:
  
  >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
  >>> newfp
  memmap([[  0.,   1.,   2.,   3.],
          [  4.,   5.,   6.,   7.],
          [  8.,   9.,  10.,  11.]], dtype=float32)
  
  Read-only memmap:
  
  >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
  >>> fpr.flags.writeable
  False
  
  Copy-on-write memmap:
  
  >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
  >>> fpc.flags.writeable
  True
  
  It's possible to assign to copy-on-write array, but values are only
  written into the memory copy of the array, and not written to disk:
  
  >>> fpc
  memmap([[  0.,   1.,   2.,   3.],
          [  4.,   5.,   6.,   7.],
          [  8.,   9.,  10.,  11.]], dtype=float32)
  >>> fpc[0,:] = 0
  >>> fpc
  memmap([[  0.,   0.,   0.,   0.],
          [  4.,   5.,   6.,   7.],
          [  8.,   9.,  10.,  11.]], dtype=float32)
  
  File on disk is unchanged:
  
  >>> fpr
  memmap([[  0.,   1.,   2.,   3.],
          [  4.,   5.,   6.,   7.],
          [  8.,   9.,  10.,  11.]], dtype=float32)
  
  Offset into a memmap:
  
  >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)
  >>> fpo
  memmap([  4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.], dtype=float32)
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code: def __getitem__(self, index):
      res = super(memmap, self).__getitem__(index)
      if type(res) is memmap and res._mmap is None:
          return res.view(type=ndarray)
      return res
  
file_path: catboost/contrib/python/numpy/py2/numpy/core/memmap.py
imports: from __future__ import division, absolute_import, print_function
  import numpy
  from .numeric import uint8, ndarray, dtype
  from numpy.compat import long, basestring, os_fspath, contextlib_nullcontext, is_pathlib_path
  from numpy.core.overrides import set_module
label_desc: Use image node_id 5 for calling the memmap obj's underlying member method code with example usage: obj.__getitem__(index) and returns: res, res