def multiply(lhs, rhs):
    """Returns element-wise product of the input arrays with broadcasting.

        Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``
        when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape,
        this is equivalent to ``mx.nd.elemwise_mul(lhs, rhs)``

    .. note::

        If the corresponding dimensions of two arrays have the same size or one of them has size 1,
        then the arrays are broadcastable to a common shape.

    Parameters
    ----------
    lhs : scalar or mxnet.ndarray.sparse.array
        First array to be multiplied.
    rhs : scalar or mxnet.ndarray.sparse.array
         Second array to be multiplied.
        If ``lhs.shape != rhs.shape``, they must be
        broadcastable to a common shape.

    Returns
    -------
    NDArray
        The element-wise multiplication of the input arrays.

    Examples
    --------
    >>> x = mx.nd.ones((2,3)).tostype('csr')
    >>> y = mx.nd.arange(2).reshape((2,1))
    >>> z = mx.nd.arange(3)
    >>> x.asnumpy()
    array([[ 1.,  1.,  1.],
           [ 1.,  1.,  1.]], dtype=float32)
    >>> y.asnumpy()
    array([[ 0.],
           [ 1.]], dtype=float32)
    >>> z.asnumpy()
    array([ 0.,  1.,  2.], dtype=float32)
    >>> (x*2).asnumpy()
    array([[ 2.,  2.,  2.],
           [ 2.,  2.,  2.]], dtype=float32)
    >>> (x*y).asnumpy()
    array([[ 0.,  0.,  0.],
           [ 1.,  1.,  1.]], dtype=float32)
    >>> mx.nd.sparse.multiply(x, y).asnumpy()
    array([[ 0.,  0.,  0.],
           [ 1.,  1.,  1.]], dtype=float32)
    >>> (x*z).asnumpy()
    array([[ 0.,  1.,  2.],
           [ 0.,  1.,  2.]], dtype=float32)
    >>> mx.nd.sparse.multiply(x, z).asnumpy()
    array([[ 0.,  1.,  2.],
           [ 0.,  1.,  2.]], dtype=float32)
    >>> z = z.reshape((1, 3))
    >>> z.asnumpy()
    array([[ 0.,  1.,  2.]], dtype=float32)
    >>> (x*z).asnumpy()
    array([[ 0.,  1.,  2.],
           [ 0.,  1.,  2.]], dtype=float32)
    >>> mx.nd.sparse.multiply(x, z).asnumpy()
    array([[ 0.,  1.,  2.],
           [ 0.,  1.,  2.]], dtype=float32)
    """
    # pylint: disable= no-member, protected-access
    if isinstance(lhs, NDArray) and isinstance(rhs, NDArray) and lhs.shape == rhs.shape:
        return _ufunc_helper(
            lhs,
            rhs,
            op.elemwise_mul,
            operator.mul,
            _internal._mul_scalar,
            None)

    return _ufunc_helper(
        lhs,
        rhs,
        op.broadcast_mul,
        operator.mul,
        _internal._mul_scalar,
        None)
def multiply ( lhs , rhs ) : if isinstance ( lhs , NDArray ) and isinstance ( rhs , NDArray ) and lhs . shape == rhs . shape : return _ufunc_helper ( lhs , rhs , op . elemwise_mul , operator . mul , _internal . _mul_scalar , None ) return _ufunc_helper ( lhs , rhs , op . broadcast_mul , operator . mul , _internal . _mul_scalar , None )
Returns element-wise product of the input arrays with broadcasting.

        Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``
        when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape,
        this is equivalent to ``mx.nd.elemwise_mul(lhs, rhs)``

    .. note::

        If the corresponding dimensions of two arrays have the same size or one of them has size 1,
        then the arrays are broadcastable to a common shape.

    Parameters
    ----------
    lhs : scalar or mxnet.ndarray.sparse.array
        First array to be multiplied.
    rhs : scalar or mxnet.ndarray.sparse.array
         Second array to be multiplied.
        If ``lhs.shape != rhs.shape``, they must be
        broadcastable to a common shape.

    Returns
    -------
    NDArray
        The element-wise multiplication of the input arrays.

    Examples
    --------
    >>> x = mx.nd.ones((2,3)).tostype('csr')
    >>> y = mx.nd.arange(2).reshape((2,1))
    >>> z = mx.nd.arange(3)
    >>> x.asnumpy()
    array([[ 1.,  1.,  1.],
           [ 1.,  1.,  1.]], dtype=float32)
    >>> y.asnumpy()
    array([[ 0.],
           [ 1.]], dtype=float32)
    >>> z.asnumpy()
    array([ 0.,  1.,  2.], dtype=float32)
    >>> (x*2).asnumpy()
    array([[ 2.,  2.,  2.],
           [ 2.,  2.,  2.]], dtype=float32)
    >>> (x*y).asnumpy()
    array([[ 0.,  0.,  0.],
           [ 1.,  1.,  1.]], dtype=float32)
    >>> mx.nd.sparse.multiply(x, y).asnumpy()
    array([[ 0.,  0.,  0.],
           [ 1.,  1.,  1.]], dtype=float32)
    >>> (x*z).asnumpy()
    array([[ 0.,  1.,  2.],
           [ 0.,  1.,  2.]], dtype=float32)
    >>> mx.nd.sparse.multiply(x, z).asnumpy()
    array([[ 0.,  1.,  2.],
           [ 0.,  1.,  2.]], dtype=float32)
    >>> z = z.reshape((1, 3))
    >>> z.asnumpy()
    array([[ 0.,  1.,  2.]], dtype=float32)
    >>> (x*z).asnumpy()
    array([[ 0.,  1.,  2.],
           [ 0.,  1.,  2.]], dtype=float32)
    >>> mx.nd.sparse.multiply(x, z).asnumpy()
    array([[ 0.,  1.,  2.],
           [ 0.,  1.,  2.]], dtype=float32)