code: def forward(self, batch):
      src = batch["units"]
      padding_mask = src == self.pad_value
  
      src = src.transpose(1, 0)
      x = self.embedding(src) * math.sqrt(self.embedding_size)
      return self.encoder_classifier(x, padding_mask)
  
imports: import math
  import torch
  import torch.nn
  from torch.nn import TransformerEncoder, TransformerEncoderLayer
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the DiscreteClassifier obj's underlying member method code with example usage: obj.forward(batch) and returns: self