text: # Copyright 2019 The TensorFlow 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.
  # ==============================================================================
  """Test configs for concat."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  from tensorflow.lite.testing.zip_test_utils import create_tensor_data
  from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
  from tensorflow.lite.testing.zip_test_utils import register_make_test_function
  
  
  @register_make_test_function()
  def make_concat_tests(options):
    """Make a set of tests to do concatenation."""
  
    test_parameters = [{
        "base_shape": [[1, 3, 4, 3], [3, 4]],
        "num_tensors": [1, 2, 3, 4, 5, 6],
        "axis": [0, 1, 2, 3, -3, -2, -1],
        "type": [tf.float32, tf.uint8, tf.int32, tf.int64],
        "fully_quantize": [False]
    }, {
        "base_shape": [[1, 3, 4, 3], [3, 4], [2, 3, 4, 3]],
        "num_tensors": [1, 2, 3, 4, 5, 6],
        "axis": [1, 2, 3, -3, -2, -1],
        "type": [tf.float32],
        "fully_quantize": [True]
    }]
  
    def get_shape(parameters, delta):
      """Return a tweaked version of 'base_shape'."""
      axis = parameters["axis"]
      shape = parameters["base_shape"][:]
      if axis < 0:
        axis += len(shape)
      if axis < len(shape):
        shape[axis] += delta
      return shape
  
    def build_graph(parameters):
      all_tensors = []
      for n in range(0, parameters["num_tensors"]):
        input_tensor = tf.compat.v1.placeholder(
            dtype=parameters["type"],
            name=("input%d" % n),
            shape=get_shape(parameters, n))
        all_tensors.append(input_tensor)
      out = tf.concat(all_tensors, parameters["axis"])
      return all_tensors, [out]
  
    def build_inputs(parameters, sess, inputs, outputs):
      all_values = []
      for n in range(0, parameters["num_tensors"]):
        input_values = create_tensor_data(
            parameters["type"],
            get_shape(parameters, n),
            min_value=-1,
            max_value=1)
        all_values.append(input_values)
      return all_values, sess.run(
          outputs, feed_dict=dict(zip(inputs, all_values)))
  
    make_zip_of_tests(
        options,
        test_parameters,
        build_graph,
        build_inputs,
        expected_tf_failures=75)
  