
    will select automatically based on memory considerations.

    * If 'sort', will use a mergesort-based approach. This will have
      a memory usage of roughly 6 times the sum of the sizes of
      `ar1` and `ar2`, not accounting for size of dtypes.
    * If 'table', will use a lookup table approach similar
      to a counting sort. This is only available for boolean and
      integer arrays. This will have a memory usage of the
      size of `ar1` plus the max-min value of `ar2`. `assume_unique`
      has no effect when the 'table' option is used.
    * If None, will automatically choose 'table' if
      the required memory allocation is less than or equal to
      6 times the sum of the sizes of `ar1` and `ar2`,
      otherwise will use 'sort'. This is done to not use
      a large amount of memory by default, even though
      'table' may be faster in most cases. If 'table' is chosen,
      `assume_unique` will have no effect.

Returns
-------
in1d : (M,) ndarray, bool
    The values `ar1[in1d]` are in `ar2`.

See Also
--------
isin                  : Version of this function that preserves the
                        shape of ar1.

Notes
-----
`in1d` can be considered as an element-wise function version of the
python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
equivalent to ``np.array([item in b for item in a])``.
However, this idea fails if `ar2` is a set, or similar (non-sequence)
container:  As ``ar2`` is converted to an array, in those cases
``asarray(ar2)`` is an object array rather than the expected array of
contained values.

Using ``kind='table'`` tends to be faster than `kind='sort'` if the
following relationship is true:
``log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927``,
but may use greater memory. The default value for `kind` will
be automatically selected based only on memory usage, so one may
manually set ``kind='table'`` if memory constraints can be relaxed.

Examples
--------
>>> import numpy as np
>>> test = np.array([0, 1, 2, 5, 0])
>>> states = [0, 2]
>>> mask = np.in1d(test, states)
>>> mask
array([ True, False,  True, False,  True])
>>> test[mask]
array([0, 2, 0])
>>> mask = np.in1d(test, states, invert=True)
>>> mask
array([False,  True, False,  True, False])
>>> test[mask]
array([1, 5])
z,`in1d` is deprecated. Use `np.isin` instead.