Cleanup Split methods

Author: ricardoV94

pytensor/tensor/basic.py

@@ -2226,21 +2226,21 @@ def make_node(self, x, axis, splits):
 
         return Apply(self, inputs, outputs)
 
-    def perform(self, node, inputs, outputs):
+    def perform(self, node, inputs, outputs_storage):
         x, axis, splits = inputs
 
         if len(splits) != self.len_splits:
             raise ValueError("Length of splits is not equal to n_splits")
-        if np.sum(splits) != x.shape[axis]:
+        if splits.sum() != x.shape[axis]:
             raise ValueError(
-                f"Split sizes sum to {np.sum(splits)}; expected {x.shape[axis]}"
+                f"Split sizes sum to {splits.sum()}; expected {x.shape[axis]}"
             )
-        if np.any(splits < 0):
+        if (splits < 0).any():
             raise ValueError("Split sizes cannot be negative")
 
         split_outs = np.split(x, np.cumsum(splits[:-1]), axis=axis)
-        for i, out in enumerate(split_outs):
-            outputs[i][0] = out
+        for out_storage, out in zip(outputs_storage, split_outs, strict=False):
+            out_storage[0] = out
 
     def infer_shape(self, fgraph, node, in_shapes):
         axis = node.inputs[1]
@@ -2254,10 +2254,10 @@ def infer_shape(self, fgraph, node, in_shapes):
             out_shapes.append(temp)
         return out_shapes
 
-    def grad(self, inputs, g_outputs):
+    def L_op(self, inputs, outputs, g_outputs):
         """Join the gradients along the axis that was used to split x."""
         x, axis, n = inputs
-        outputs = self(*inputs, return_list=True)
+
         # If all the output gradients are disconnected, then so are the inputs
         if builtins.all(isinstance(g.type, DisconnectedType) for g in g_outputs):
             return [