code: def __init__(
      self,
      vocab_size,
      embedding_size,
      n_heads,
      hidden_size,
      n_layers,
      dropout,
      pad_value,
      total_speakers,
  ):
      super().__init__()
      self.pad_value = (
          pad_value.item() if torch.is_tensor(pad_value) else pad_value
      )
  
      self.embedding = torch.nn.Embedding(vocab_size, embedding_size)
      self.embedding_size = embedding_size
      torch.nn.init.normal_(
          self.embedding.weight, mean=0, std=self.embedding_size**-0.5
      )
  
      self.encoder_classifier = Classifier(
          embedding_size,
          n_heads,
          hidden_size,
          n_layers,
          dropout,
          total_speakers,
      )
  
imports: import math
  import torch
  import torch.nn
  from torch.nn import TransformerEncoder, TransformerEncoderLayer
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new DiscreteClassifier object from inherited base classes: torch.nn with example: obj = DiscreteClassifier(vocab_size, embedding_size, n_heads, hidden_size, n_layers, dropout, pad_value, total_speakers)