Remove unused numba dispatch function

pytensor/link/numba/dispatch/elemwise.py

@@ -1,15 +1,12 @@
-from collections.abc import Callable
 from functools import singledispatch
 from textwrap import dedent, indent
-from typing import Any
 
 import numba
 import numpy as np
 from numba.core.extending import overload
 from numpy.core.numeric import normalize_axis_index, normalize_axis_tuple
 
 from pytensor import config
-from pytensor.graph.basic import Apply
 from pytensor.graph.op import Op
 from pytensor.link.numba.dispatch import basic as numba_basic
 from pytensor.link.numba.dispatch.basic import (
@@ -124,42 +121,6 @@ def scalar_in_place_fn_ScalarMinimum(op, idx, res, arr):
 """
 
 
-def create_vectorize_func(
-    scalar_op_fn: Callable,
-    node: Apply,
-    use_signature: bool = False,
-    identity: Any | None = None,
-    **kwargs,
-) -> Callable:
-    r"""Create a vectorized Numba function from a `Apply`\s Python function."""
-
-    if len(node.outputs) > 1:
-        raise NotImplementedError(
-            "Multi-output Elemwise Ops are not supported by the Numba backend"
-        )
-
-    if use_signature:
-        signature = [create_numba_signature(node, force_scalar=True)]
-    else:
-        signature = []
-
-    target = (
-        getattr(node.tag, "numba__vectorize_target", None)
-        or config.numba__vectorize_target
-    )
-
-    numba_vectorized_fn = numba_basic.numba_vectorize(
-        signature, identity=identity, target=target, fastmath=config.numba__fastmath
-    )
-
-    py_scalar_func = getattr(scalar_op_fn, "py_func", scalar_op_fn)
-
-    elemwise_fn = numba_vectorized_fn(scalar_op_fn)
-    elemwise_fn.py_scalar_func = py_scalar_func
-
-    return elemwise_fn
-
-
 def create_multiaxis_reducer(
     scalar_op,
     identity,