Remove dangling TensorSolve class

theano/tensor/nlinalg.py

@@ -700,35 +700,3 @@ def norm(x, ord):
             raise ValueError(0)
     elif ndim > 2:
         raise NotImplementedError("We don't support norm witn ndim > 2")
-
-
-class TensorSolve(Op):
-    """Computes the pseudo-inverse of a matrix :math:`A`.
-
-    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`.
-
-    Note that :math:`Ax=AA^+b`, so :math:`AA^+` is close to the identity matrix.
-    This method is not faster then `matrix_inverse`. Its strength comes from
-    that it works for non-square matrices.
-    If you have a square matrix though, `matrix_inverse` can be both more
-    exact and faster to compute. Also this op does not get optimized into a
-    solve op.
-    """
-
-    __props__ = ()
-
-    def __init__(self):
-        pass
-
-    def make_node(self, x):
-        x = as_tensor_variable(x)
-        assert x.ndim == 2
-        return Apply(self, [x], [x.type()])
-
-    def perform(self, node, (x,), (z, )):
-        z[0] = numpy.linalg.pinv(x).astype(x.dtype)
-
-tensorsolve = TensorSolve()