remove redundant code

x2paddle/op_mapper/tf_op_mapper.py

@@ -1122,37 +1122,6 @@ class TFOpMapper(OpMapper):
                                   output=node,
                                   param_attr=None)
 
-    def RandomUniform(self, node):
-        shape = self.graph.get_node(node.layer.input[0], copy=True)
-        self.add_omit_nodes(shape.layer_name, node.layer_name)
-        if shape.layer_type == "Const":
-            shape = shape.value.tolist()
-        else:
-            shape = self.decoder.infer_shape_tensor(shape)
-        if node.tf_data_format == "NHWC" and len(shape) == 4:
-            shape = [shape[i] for i in [0, 3, 1, 2]]
-        attr = {"shape": shape, "min": 0.0, "max": 0.9999}
-        if shape[0] < 0:
-            input = self.batch_node
-            node.fluid_code.add_layer("uniform_random_batch_size_like",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
-        else:
-            node.fluid_code.add_layer("uniform_random",
-                                      inputs=None,
-                                      output=node,
-                                      param_attr=attr)
-
-    def GreaterEqual(self, node):
-        x = self.graph.get_node(node.layer.input[0], copy=True)
-        y = self.graph.get_node(node.layer.input[1], copy=True)
-        inputs = {"x": x, "y": y}
-        node.fluid_code.add_layer("greater_equal",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
-
     def RandomUniform(self, node):
         shape = self.graph.get_node(node.layer.input[0], copy=True)
         self.add_omit_nodes(shape.layer_name, node.layer_name)