def prod(a, axis=None, dtype=None, out=None, keepdims=False):
if type(a) is not mu.ndarray:<EOL><INDENT>try:<EOL><INDENT>prod = a.prod<EOL><DEDENT>except AttributeError:<EOL><INDENT>return _methods._prod(a, axis=axis, dtype=dtype,<EOL>out=out, keepdims=keepdims)<EOL><DEDENT>return prod(axis=axis, dtype=dtype, out=out)<EOL><DEDENT>else:<EOL><INDENT>return _methods._prod(a, axis=axis, dtype=dtype,<EOL>out=out, keepdims=keepdims)<EOL><DEDENT>
Return the product of array elements over a given axis.

Parameters
----------
a : array_like
    Input data.
axis : None or int or tuple of ints, optional
    Axis or axes along which a product is performed.
    The default (`axis` = `None`) is perform a product over all
    the dimensions of the input array. `axis` may be negative, in
    which case it counts from the last to the first axis.

    .. versionadded:: 1.7.0

    If this is a tuple of ints, a product is performed on multiple
    axes, instead of a single axis or all the axes as before.
dtype : data-type, optional
    The data-type of the returned array, as well as of the accumulator
    in which the elements are multiplied.  By default, if `a` is of
    integer type, `dtype` is the default platform integer. (Note: if
    the type of `a` is unsigned, then so is `dtype`.)  Otherwise,
    the dtype is the same as that of `a`.
out : ndarray, optional
    Alternative output array in which to place the result. It must have
    the same shape as the expected output, but the type of the
    output values will be cast if necessary.
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the original `arr`.

Returns
-------
product_along_axis : ndarray, see `dtype` parameter above.
    An array shaped as `a` but with the specified axis removed.
    Returns a reference to `out` if specified.

See Also
--------
ndarray.prod : equivalent method
numpy.doc.ufuncs : Section "Output arguments"

Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.  That means that, on a 32-bit platform:

>>> x = np.array([536870910, 536870910, 536870910, 536870910])
>>> np.prod(x) #random
16

Examples
--------
By default, calculate the product of all elements:

>>> np.prod([1.,2.])
2.0

Even when the input array is two-dimensional:

>>> np.prod([[1.,2.],[3.,4.]])
24.0

But we can also specify the axis over which to multiply:

>>> np.prod([[1.,2.],[3.,4.]], axis=1)
array([  2.,  12.])

If the type of `x` is unsigned, then the output type is
the unsigned platform integer:

>>> x = np.array([1, 2, 3], dtype=np.uint8)
>>> np.prod(x).dtype == np.uint
True

If `x` is of a signed integer type, then the output type
is the default platform integer:

>>> x = np.array([1, 2, 3], dtype=np.int8)
>>> np.prod(x).dtype == np.int
True