You could just iterate the input x:
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(20, 10)
        self.fc2 = nn.Linear(10, 2)
        
    def forward(self, x):
        for x_ in x:
            print(x_.shape)
        
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x


model = MyModel()
x = torch.randn(2, 20)
output = model(x)

What is your use case? Since the batch size is usually varying, you shouldn’t assume to get a specific batch size.
The most common approach is to write code that works with different batch sizes.