def METHOD_NAME(self):
    class Net(torch.nn.Module):
        def forward(self, x, y):
            z = torch.bmm(x, y)
            z = torch.flatten(z, 1)
            return z
    def training_iter_fn(batch, model, optimizer):
        optimizer.zero_grad()
        out = model(**batch)
        target = torch.tensor([0, 7])
        loss = torch.nn.CrossEntropyLoss()(out, target)
        loss.backward()
        optimizer.step()
        return loss
    net = Net()
    input1 = torch.randn(2, 1, 4)
    input2 = torch.randn(2, 4, 8, requires_grad=True)
    optimizer = torch.optim.Adam([input2], lr=0.1)
    cnts = torch._dynamo.testing.CompileCounter()
    opt_training_iter_fn = torch._dynamo.optimize(cnts)(training_iter_fn)
    batch = {"x": input1, "y": input2}
    for _ in range(2):
        opt_training_iter_fn(batch, net, optimizer)
    self.assertEqual(cnts.frame_count, 2)