# Buggy code
def pinv(a, rcond=1e-15):
    """
    Compute the (Moore-Penrose) pseudo-inverse of a matrix.

    Calculate the generalized inverse of a matrix using its
    singular-value decomposition (SVD) and including all
    *large* singular values.

    Parameters
    ----------
    a : (M, N) array_like
      Matrix to be pseudo-inverted.
    rcond : float
      Cutoff for small singular values.
      Singular values smaller (in modulus) than
      `rcond` * largest_singular_value (again, in modulus)
      are set to zero.

    Returns
    -------
    B : (N, M) ndarray
      The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so
      is `B`.

    Raises
    ------
    LinAlgError
      If the SVD computation does not converge.

    Notes
    -----
    The pseudo-inverse of a matrix A, denoted :math:`A^+`, is
    defined as: "the matrix that 'solves' [the least-squares problem]
    :math:`Ax = b`," i.e., if :math:`\\bar{x}` is said solution, then
    :math:`A^+` is that matrix such that :math:`\\bar{x} = A^+b`.

    It can be shown that if :math:`Q_1 \\Sigma Q_2^T = A` is the singular
    value decomposition of A, then
    :math:`A^+ = Q_2 \\Sigma^+ Q_1^T`, where :math:`Q_{1,2}` are
    orthogonal matrices, :math:`\\Sigma` is a diagonal matrix consisting
    of A's so-called singular values, (followed, typically, by
    zeros), and then :math:`\\Sigma^+` is simply the diagonal matrix
    consisting of the reciprocals of A's singular values
    (again, followed by zeros). [1]_

    References
    ----------
    .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
           FL, Academic Press, Inc., 1980, pp. 139-142.

    Examples
    --------
    The following example checks that ``a * a+ * a == a`` and
    ``a+ * a * a+ == a+``:

    >>> a = np.random.randn(9, 6)
    >>> B = np.linalg.pinv(a)
    >>> np.allclose(a, np.dot(a, np.dot(B, a)))
    True
    >>> np.allclose(B, np.dot(B, np.dot(a, B)))
    True

    """
    a, wrap = _makearray(a)
    if _isEmpty2d(a):
        res = empty(a.shape[:-2] + (a.shape[-1], a.shape[-2]), dtype=a.dtype)
        return wrap(res)
    a = a.conjugate()
    u, s, vt = svd(a, full_matrices=False)
    m = u.shape[0]
    n = vt.shape[1]
    cutoff = rcond * maximum.reduce(s)
    for i in range(min(n, m)):
        if s[i] > cutoff:
            s[i] = 1.0 / s[i]
        else:
            s[i] = 0.0
    res = dot(transpose(vt), multiply(s[:, newaxis], transpose(u)))
    return wrap(res)


# Traceback (ValueError)
a = np.stack((np.eye(3),)*4, axis=0)
ai = np.linalg.inv(a)
assert (a == ai).all()
api = np.linalg.pinv(a)
Traceback (most recent call last):
File "<pyshell#9>", line 1, in <module>
np.linalg.pinv(a)
File "C:\Program Files\Python 3.5\lib\site-packages\numpy\linalg\linalg.py", line 1668, in pinv
if s[i] > cutoff:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

# Fixed code
def pinv(a, rcond=1e-15):
    """
    Compute the (Moore-Penrose) pseudo-inverse of a matrix.

    Calculate the generalized inverse of a matrix using its
    singular-value decomposition (SVD) and including all
    *large* singular values.

    .. versionchanged:: 1.14
       Can now operate on stacks of matrices

    Parameters
    ----------
    a : (..., M, N) array_like
        Matrix or stack of matrices to be pseudo-inverted.
    rcond : (...) array_like of float
        Cutoff for small singular values.
        Singular values smaller (in modulus) than
        `rcond` * largest_singular_value (again, in modulus)
        are set to zero. Broadcasts against the stack of matrices

    Returns
    -------
    B : (..., N, M) ndarray
        The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so
        is `B`.

    Raises
    ------
    LinAlgError
        If the SVD computation does not converge.

    Notes
    -----
    The pseudo-inverse of a matrix A, denoted :math:`A^+`, is
    defined as: "the matrix that 'solves' [the least-squares problem]
    :math:`Ax = b`," i.e., if :math:`\\bar{x}` is said solution, then
    :math:`A^+` is that matrix such that :math:`\\bar{x} = A^+b`.

    It can be shown that if :math:`Q_1 \\Sigma Q_2^T = A` is the singular
    value decomposition of A, then
    :math:`A^+ = Q_2 \\Sigma^+ Q_1^T`, where :math:`Q_{1,2}` are
    orthogonal matrices, :math:`\\Sigma` is a diagonal matrix consisting
    of A's so-called singular values, (followed, typically, by
    zeros), and then :math:`\\Sigma^+` is simply the diagonal matrix
    consisting of the reciprocals of A's singular values
    (again, followed by zeros). [1]_

    References
    ----------
    .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
           FL, Academic Press, Inc., 1980, pp. 139-142.

    Examples
    --------
    The following example checks that ``a * a+ * a == a`` and
    ``a+ * a * a+ == a+``:

    >>> a = np.random.randn(9, 6)
    >>> B = np.linalg.pinv(a)
    >>> np.allclose(a, np.dot(a, np.dot(B, a)))
    True
    >>> np.allclose(B, np.dot(B, np.dot(a, B)))
    True

    """
    a, wrap = _makearray(a)
    rcond = asarray(rcond)
    if _isEmpty2d(a):
        res = empty(a.shape[:-2] + (a.shape[-1], a.shape[-2]), dtype=a.dtype)
        return wrap(res)
    a = a.conjugate()
    u, s, vt = svd(a, full_matrices=False)

    # discard small singular values
    cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True)
    large = s > cutoff
    s = divide(1, s, where=large, out=s)
    s[~large] = 0

    res = matmul(transpose(vt), multiply(s[..., newaxis], transpose(u)))
    return wrap(res)