Remove duplicated `Inv` Op

pytensor/link/numba/dispatch/nlinalg.py

@@ -14,7 +14,6 @@ from pytensor.tensor.nlinalg import (
     Det,
     Eig,
     Eigh,
-    Inv,
     MatrixInverse,
     MatrixPinv,
     QRFull,
@@ -125,18 +124,6 @@ def numba_funcify_Eigh(op, node, **kwargs):
     return eigh
 
 
-@numba_funcify.register(Inv)
-def numba_funcify_Inv(op, node, **kwargs):
-    out_dtype = node.outputs[0].type.numpy_dtype
-    inputs_cast = int_to_float_fn(node.inputs, out_dtype)
-
-    @numba_basic.numba_njit(inline="always")
-    def inv(x):
-        return np.linalg.inv(inputs_cast(x)).astype(out_dtype)
-
-    return inv
-
-
 @numba_funcify.register(MatrixInverse)
 def numba_funcify_MatrixInverse(op, node, **kwargs):
     out_dtype = node.outputs[0].type.numpy_dtype

pytensor/tensor/nlinalg.py

@@ -78,25 +78,6 @@ def pinv(x, hermitian=False):
     return MatrixPinv(hermitian=hermitian)(x)
 
 
-class Inv(Op):
-    """Computes the inverse of one or more matrices."""
-
-    def make_node(self, x):
-        x = as_tensor_variable(x)
-        return Apply(self, [x], [x.type()])
-
-    def perform(self, node, inputs, outputs):
-        (x,) = inputs
-        (z,) = outputs
-        z[0] = np.linalg.inv(x).astype(x.dtype)
-
-    def infer_shape(self, fgraph, node, shapes):
-        return shapes
-
-
-inv = Inv()
-
-
 class MatrixInverse(Op):
     r"""Computes the inverse of a matrix :math:`A`.
 
@@ -169,7 +150,7 @@ class MatrixInverse(Op):
         return shapes
 
 
-matrix_inverse = MatrixInverse()
+inv = matrix_inverse = MatrixInverse()
 
 
 def matrix_dot(*args):

tests/link/numba/test_nlinalg.py

@@ -352,26 +352,6 @@ def test_Eigh(x, uplo, exc):
             None,
             (),
         ),
-        (
-            nlinalg.Inv,
-            set_test_value(
-                at.dmatrix(),
-                (lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
-            ),
-            None,
-            (),
-        ),
-        (
-            nlinalg.Inv,
-            set_test_value(
-                at.lmatrix(),
-                (lambda x: x.T.dot(x))(
-                    rng.integers(1, 10, size=(3, 3)).astype("int64")
-                ),
-            ),
-            None,
-            (),
-        ),
         (
             nlinalg.MatrixPinv,
             set_test_value(