Remove duplicated Solve dispatch

pytensor/link/numba/dispatch/basic.py

@@ -8,8 +8,6 @@ from textwrap import dedent
 import numba
 import numba.np.unsafe.ndarray as numba_ndarray
 import numpy as np
-import scipy
-import scipy.special
 from llvmlite import ir
 from numba import types
 from numba.core.errors import NumbaWarning, TypingError
@@ -36,7 +34,6 @@ from pytensor.tensor.basic import Nonzero
 from pytensor.tensor.blas import BatchedDot
 from pytensor.tensor.math import Dot
 from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
-from pytensor.tensor.slinalg import Solve
 from pytensor.tensor.sort import ArgSortOp, SortOp
 from pytensor.tensor.type import TensorType
 from pytensor.tensor.type_other import MakeSlice, NoneConst
@@ -626,51 +623,6 @@ def numba_funcify_Dot(op, node, **kwargs):
     return dot_with_cast
 
 
-@numba_funcify.register(Solve)
-def numba_funcify_Solve(op, node, **kwargs):
-    assume_a = op.assume_a
-    # check_finite = op.check_finite
-
-    if assume_a != "gen":
-        lower = op.lower
-
-        warnings.warn(
-            (
-                "Numba will use object mode to allow the "
-                "`compute_uv` argument to `numpy.linalg.svd`."
-            ),
-            UserWarning,
-        )
-
-        ret_sig = get_numba_type(node.outputs[0].type)
-
-        @numba_njit
-        def solve(a, b):
-            with numba.objmode(ret=ret_sig):
-                ret = scipy.linalg.solve_triangular(
-                    a,
-                    b,
-                    lower=lower,
-                    # check_finite=check_finite
-                )
-            return ret
-
-    else:
-        out_dtype = node.outputs[0].type.numpy_dtype
-        inputs_cast = int_to_float_fn(node.inputs, out_dtype)
-
-        @numba_njit
-        def solve(a, b):
-            return np.linalg.solve(
-                inputs_cast(a),
-                inputs_cast(b),
-                # assume_a=assume_a,
-                # check_finite=check_finite,
-            ).astype(out_dtype)
-
-    return solve
-
-
 @numba_funcify.register(BatchedDot)
 def numba_funcify_BatchedDot(op, node, **kwargs):
     dtype = node.outputs[0].type.numpy_dtype