text: # Copyright 2022 Sony Semiconductors Israel, Inc. 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.
  # ==============================================================================
  import torch
  
  from model_compression_toolkit.core.pytorch.utils import to_torch_tensor
  from tests.pytorch_tests.model_tests.base_pytorch_test import BasePytorchTest
  
  """
  This tests check a model with a parameter which is a constant at inference time. 
  In addition, the model has an addition layer regular constant tensor
  """
  class ParameterNet(torch.nn.Module):
      def __init__(self):
          super(ParameterNet, self).__init__()
          self.conv1 = torch.nn.Conv2d(3, 4, kernel_size=1, stride=1)
          self.p = torch.nn.Parameter(torch.ones(1) * 1.5)
  
      def forward(self, x):
          x = self.conv1(x)
          x = x + to_torch_tensor(torch.ones(1))
          return x * self.p
  
  
  class ParameterNetTest(BasePytorchTest):
      """
      This tests check a model with a parameter which is a constant at inference time.
      In addition, the model has an addition layer regular constant tensor
      """
      def __init__(self, unit_test):
          super().__init__(unit_test)
  
      def create_feature_network(self, input_shape):
          return ParameterNet()