input: Write a python function to instantiate and test a neural network model using the provided NeuralNet class.
  assert isinstance(model, NeuralNet)
  assert hasattr(model, 'embedding')
  assert hasattr(model, 'fc')
  assert isinstance(model.embedding, nn.Embedding)
  assert isinstance(model.fc, nn.Linear)
output: def test_neural_network():
      import torch.nn as nn
  
      class NeuralNet(nn.Module):
          def __init__(self, vocab_size, embed_dim, output_dim):
              super(NeuralNet, self).__init__()
              self.embedding = nn.Embedding(vocab_size, embed_dim)
              self.fc = nn.Linear(embed_dim, output_dim)
  
          def forward(self, x):
              x = self.embedding(x)
              x = x.mean(dim=1)
              x = self.fc(x)
              return x
  
      # Instantiate the model
      vocab_size = 1000
      embed_dim = 128
      output_dim = 10
      model = NeuralNet(vocab_size, embed_dim, output_dim)
  
      # Assertions
      assert isinstance(model, NeuralNet)
      assert hasattr(model, 'embedding')
      assert hasattr(model, 'fc')
      assert isinstance(model.embedding, nn.Embedding)
      assert isinstance(model.fc, nn.Linear)