You might consider inheriting from the Sequential module and using a layer list to make your module a bit easier to reason about:
(I have nn.functional imported as nnf)
class MultiClass(nn.Sequential):
    
    def __init__(self, z_dim):
        super(MultiClass, self).__init__()

        layers = [
            ("cf1" , nn.Linear(z_dim, z_dim)),
            ("cf1N", nn.BatchNorm1d(num_features = z_dim)),
            ("r1"  , nn.ReLU()),
            ("cf1D", nn.Dropout(0.5) ),
            ("cf2" , nn.Linear(z_dim, z_dim)),
            ("cf2N", nn.BatchNorm1d(num_features = z_dim)),
            ("r2"  , nn.ReLU()),
            ("cf2D", nn.Dropout(0.5)),
            ("cf3" , nn.Linear(z_dim, 32)),
        ]
        
        [self.add_module(name, layer) for (name, layer) in layers]

        
    def classifier(self,z):
        return nnf.softmax(self(z))

x_dim also does not seem to be used anywhere?
MultiClass(
  (cf1): Linear(in_features=2000, out_features=2000, bias=True)
  (cf1N): BatchNorm1d(2000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (cf1D): Dropout(p=0.5)
  (cf2): Linear(in_features=2000, out_features=2000, bias=True)
  (cf2N): BatchNorm1d(2000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (cf2D): Dropout(p=0.5)
  (cf3): Linear(in_features=2000, out_features=32, bias=True)
)

How is your data structured?
Most classification tasks are accomplished by predicting probabilities over a tensor with the rank of the number of classes rather than predicting the index of the class directly. E.g. ImageNet 4 has 1000 categories so a label for gold fish would have the value 1.0 at the index 1 on a rank 1000 tensor.
Sorry if I’m telling you what you already know! But some more detail about the code/task would be helpful. 
Another thing to remember:
The domain of the softmax function is [0, 1]. So the result of your .classifier() method on your example label would be something like:
>>> nnf.softmax(torch.tensor([2, 5, 31, 7]).float())
tensor([2.5437e-13, 5.1091e-12, 1.0000e+00, 3.7751e-11])