input: Write a Python function to create an instance of the MnistNet class and pass a random input tensor through the network.
  ```python
  import torch 
  import torch.nn as nn
  import torch.nn.functional as F
  
  class MnistNet(nn.Module): 
      def __init__(self): 
          super(MnistNet, self).__init__() 
          self.conv1 = nn.Conv2d(1, 10, kernel_size=5) 
          self.conv2 = nn.Conv2d(10, 20, kernel_size=5) 
          self.conv2_drop = nn.Dropout2d() 
          self.fc1 = nn.Linear(320, 50)
          self.fc2 = nn.Linear(50, 10)
      def forward(self, x):
          x = F.relu(F.max_pool2d(self.conv1(x), 2)) 
          x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
          x = x.view(-1, 320) 
          x = F.relu(self.fc1(x))
          x = F.dropout(x, training=self.training) 
          x = self.fc2(x) 
          return F.log_softmax(x)
  
  def test_mnistnet():
      model = MnistNet()
      input_tensor = torch.randn(1, 1, 28, 28)
      output = model(input_tensor)
      assert output.shape == (1, 10)
      assert torch.is_tensor(output)
      assert output.size(1) == 10
  
  test_mnistnet()
output: def test_mnistnet():
      model = MnistNet()
      input_tensor = torch.randn(1, 1, 28, 28)
      output = model(input_tensor)
      assert output.shape == (1, 10)
      assert torch.is_tensor(output)
      assert output.size(1) == 10
  
  test_mnistnet()