Arm backend: Replace asserts with report_reject in operator_support

backends/arm/operator_support/to_copy_support.py

@@ -76,8 +76,6 @@ def _merge_supported_types(
     def is_node_tosa_supported(
         self, node: fx.Node, tosa_spec: TosaSpecification
     ) -> bool:
-        assert node.target in self.targets
-
         supported_dtypes = (
             self.ALL_SUPPORTED_TYPES
             if tosa_spec.support_float()
@@ -90,10 +88,27 @@ def is_node_tosa_supported(
             if v in supported_dtypes
         )
 
-        # Check input type
-        assert len(node.all_input_nodes) == 1
+        if len(node.all_input_nodes) != 1:
+            self.reporter.report_reject(
+                node,
+                (
+                    "Expected exactly one input node, "
+                    f"got {len(node.all_input_nodes)} for {node.target}."
+                ),
+            )
+            return False
         input_val = node.all_input_nodes[0].meta["val"]
-        assert isinstance(input_val, torch._subclasses.FakeTensor)
+        if not isinstance(input_val, torch._subclasses.FakeTensor):
+            self.reporter.report_reject(
+                node,
+                (
+                    "Invalid or missing meta: expected FakeTensor input, got "
+                    f"{type(input_val).__name__} for {node.target}."
+                ),
+            )
+            return False
+
+        # Check input type
         input_dtype = input_val.dtype
         if input_dtype not in supported_dtypes:
             self.reporter.report_reject(
@@ -104,14 +119,24 @@ def is_node_tosa_supported(
 
         # Check output type
         output_val = node.meta["val"]
-        assert isinstance(output_val, torch._subclasses.FakeTensor)
+        if not isinstance(output_val, torch._subclasses.FakeTensor):
+            self.reporter.report_reject(
+                node,
+                (
+                    "Invalid or missing meta: expected FakeTensor output, got "
+                    f"{type(output_val).__name__} for {node.target}."
+                ),
+            )
+            return False
         if output_val.dtype not in supported_dtypes[input_dtype]:
             self.reporter.report_reject(
                 node,
-                f"Output dtype {output_val.dtype} is not supported in "
-                f"{node.target} for input dtype {input_dtype}. "
-                f"Supported output types: "
-                f"{''.join(str(t) for t in supported_dtypes[input_dtype])}",
+                (
+                    f"Output dtype {output_val.dtype} is not supported in "
+                    f"{node.target} for input dtype {input_dtype}. "
+                    f"Supported output types: "
+                    f"{', '.join(str(t) for t in supported_dtypes[input_dtype])}"
+                ),
             )
             return False
 
@@ -120,8 +145,10 @@ def is_node_tosa_supported(
             if node.kwargs["memory_format"] in (torch.preserve_format,):
                 self.reporter.report_reject(
                     node,
-                    f"Argument 'memory_format' is not supported for "
-                    f"{node.target} right now.",
+                    (
+                        "Argument 'memory_format' is not supported for "
+                        f"{node.target} right now."
+                    ),
                 )
                 return False
 
@@ -132,8 +159,10 @@ def is_node_tosa_supported(
             if dim_order != list(range(len(dim_order))):  # type: ignore[arg-type]
                 self.reporter.report_reject(
                     node,
-                    f"Argument {dim_order=} is not supported for "
-                    f"{node.target} right now.",
+                    (
+                        f"Argument {dim_order=} is not supported for "
+                        f"{node.target} right now."
+                    ),
                 )
                 return False