new_contents: # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  # 
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  # 
  #     http://www.apache.org/licenses/LICENSE-2.0
  # 
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
  from program_config import TensorConfig, ProgramConfig
  import numpy as np
  import paddle.inference as paddle_infer
  from functools import partial
  from typing import Optional, List, Callable, Dict, Any, Set
  import unittest
  
  
  class TrtConvertTileTest(TrtLayerAutoScanTest):
      def is_program_valid(self, program_config: ProgramConfig) -> bool:
          inputs = program_config.inputs
          attrs = [
              program_config.ops[i].attrs
              for i in range(len(program_config.ops))
          ]
          for x in attrs[0]['repeat_times']:
              if x <= 0:
                  return False
  
          return True
  
      def sample_program_configs(self):
          def generate_input1(attrs: List[Dict[str, Any]]):
              return np.ones([1, 2, 3, 4]).astype(np.float32)
  
          for repeat_times in [[100], [1, 2], [0, 3], [1, 2, 100]]:
              dics = [{"repeat_times": repeat_times}]
  
              ops_config = [{
                  "op_type": "tile",
                  "op_inputs": {
                      "X": ["input_data"]
                  },
                  "op_outputs": {
                      "Out": ["tile_output_data"]
                  },
                  "op_attrs": dics[0]
              }]
              ops = self.generate_op_config(ops_config)
  
              program_config = ProgramConfig(
                  ops=ops,
                  weights={},
                  inputs={
                      "input_data": TensorConfig(data_gen=partial(generate_input1,
                                                                  dics))
                  },
                  outputs=["tile_output_data"])
  
              yield program_config
  
      def sample_predictor_configs(
              self, program_config) -> (paddle_infer.Config, List[int], float):
          def generate_dynamic_shape(attrs):
              self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
              self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
              self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
  
          def clear_dynamic_shape():
              self.dynamic_shape.min_input_shape = {}
              self.dynamic_shape.max_input_shape = {}
              self.dynamic_shape.opt_input_shape = {}
  
          def generate_trt_nodes_num(attrs, dynamic_shape):
              if dynamic_shape == True:
                  return 0, 3
              else:
                  return 1, 2
  
          attrs = [
              program_config.ops[i].attrs
              for i in range(len(program_config.ops))
          ]
  
          # for static_shape
          clear_dynamic_shape()
          self.trt_param.precision = paddle_infer.PrecisionType.Float32
          yield self.create_inference_config(), generate_trt_nodes_num(
              attrs, False), 1e-5
          self.trt_param.precision = paddle_infer.PrecisionType.Half
          yield self.create_inference_config(), generate_trt_nodes_num(
              attrs, False), 1e-4
  
          # for dynamic_shape
          generate_dynamic_shape(attrs)
          self.trt_param.precision = paddle_infer.PrecisionType.Float32
          yield self.create_inference_config(), generate_trt_nodes_num(attrs,
                                                                       True), 1e-5
          self.trt_param.precision = paddle_infer.PrecisionType.Half
          yield self.create_inference_config(), generate_trt_nodes_num(attrs,
                                                                       True), 1e-4
  
      def test(self):
          self.run_test()
  
  
  if __name__ == "__main__":
      unittest.main()
  
subject: add test (#35568)