def take(a, indices, axis=None, out=None, mode='<STR_LIT>'):
try:<EOL><INDENT>take = a.take<EOL><DEDENT>except AttributeError:<EOL><INDENT>return _wrapit(a, '<STR_LIT>', indices, axis, out, mode)<EOL><DEDENT>return take(indices, axis, out, mode)<EOL>
Take elements from an array along an axis.

This function does the same thing as "fancy" indexing (indexing arrays
using arrays); however, it can be easier to use if you need elements
along a given axis.

Parameters
----------
a : array_like
    The source array.
indices : array_like
    The indices of the values to extract.

    .. versionadded:: 1.8.0

    Also allow scalars for indices.
axis : int, optional
    The axis over which to select values. By default, the flattened
    input array is used.
out : ndarray, optional
    If provided, the result will be placed in this array. It should
    be of the appropriate shape and dtype.
mode : {'raise', 'wrap', 'clip'}, optional
    Specifies how out-of-bounds indices will behave.

    * 'raise' -- raise an error (default)
    * 'wrap' -- wrap around
    * 'clip' -- clip to the range

    'clip' mode means that all indices that are too large are replaced
    by the index that addresses the last element along that axis. Note
    that this disables indexing with negative numbers.

Returns
-------
subarray : ndarray
    The returned array has the same type as `a`.

See Also
--------
compress : Take elements using a boolean mask
ndarray.take : equivalent method

Examples
--------
>>> a = [4, 3, 5, 7, 6, 8]
>>> indices = [0, 1, 4]
>>> np.take(a, indices)
array([4, 3, 6])

In this example if `a` is an ndarray, "fancy" indexing can be used.

>>> a = np.array(a)
>>> a[indices]
array([4, 3, 6])

If `indices` is not one dimensional, the output also has these dimensions.

>>> np.take(a, [[0, 1], [2, 3]])
array([[4, 3],
       [5, 7]])