deal with comments

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -740,30 +740,27 @@ class OpSet9():
     def Unsqueeze(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         axes = node.get_attr('axes')
-        if axes is None:
-            axes = self.graph.get_input_node(node, idx=1, copy=True)
-            axes = _const_weight_or_none(axes)
-        if len(val_x.out_shapes[0]) == 0 and len(axes) == 1 and axes[0] == 0:
-            if node.name:
+        if axes is not None:
+            if len(val_x.out_shapes[0]) == 0 and len(axes) == 1 and axes[
+                    0] == 0:
                 self.paddle_graph.add_layer(
                     'paddle.reshape',
                     inputs={"x": val_x.name},
                     outputs=[node.name],
                     shape=[1])
-        else:
-            if isinstance(axes, list) or isinstance(axes, tuple) or isinstance(
-                    axes, np.ndarray):
+            else:
                 self.paddle_graph.add_layer(
                     'paddle.unsqueeze',
                     inputs={"x": val_x.name},
                     axis=axes,
                     outputs=[node.name])
-            else:
-                self.paddle_graph.add_layer(
-                    'paddle.unsqueeze',
-                    inputs={"x": val_x.name,
-                            "axis": axes.name},
-                    outputs=[node.name])
+        else:
+            axes = self.graph.get_input_node(node, idx=1, copy=True)
+            self.paddle_graph.add_layer(
+                'paddle.unsqueeze',
+                inputs={"x": val_x.name,
+                        "axis": axes.name},
+                outputs=[node.name])
 
     @print_mapping_info
     def Shrink(self, node):
@@ -896,7 +893,7 @@ class OpSet9():
     def Gather(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         indices = self.graph.get_input_node(node, idx=1, copy=True)
-        indices_values = _const_weight_or_none(indices)
+        indices_values = _const_weight_or_none(indices, necessary=True)
         if isinstance(indices_values, np.ndarray):
             indices_values = indices_values.tolist()
         indices_shape = indices.out_shapes[0]