input: Write a python function to initialize and return a CNN model in PyTorch.
  assert isinstance(initialize_cnn_model(), nn.Module)
  assert isinstance(initialize_cnn_model().conv1, nn.Sequential)
  assert isinstance(initialize_cnn_model().fc3, nn.Linear)
output: import torch.nn as nn
  import torch.nn.functional as F
  
  def initialize_cnn_model():
      class CNNModel(nn.Module):
          def __init__(self):
              super(CNNModel, self).__init__()
              self.conv1 = nn.Sequential(
                  nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1),
                  nn.ReLU(),
                  nn.MaxPool2d(kernel_size=2, stride=2)
              )
              self.conv2 = nn.Sequential(
                  nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),
                  nn.ReLU(),
                  nn.MaxPool2d(kernel_size=2, stride=2)
              )
              self.fc1 = nn.Linear(in_features=64 * 7 * 7, out_features=128)
              self.fc2 = nn.Linear(in_features=128, out_features=64)
              self.fc3 = nn.Linear(in_features=64, out_features=10)
          
          def forward(self, x):
              x = self.conv1(x)
              x = self.conv2(x)
              x = x.view(x.size(0), -1)
              x = F.relu(self.fc1(x))
              x = F.relu(self.fc2(x))
              x = self.fc3(x)
              return x
      
      return CNNModel()