class_docstr: Combines decoder_embedding with the tokens_head layer to simulate the classifier in NemoNMT
class_docstr_tok: ["Combines","decoder_embedding","with","the","tokens_head","layer","to","simulate","the","classifier","in","NemoNMT"]
code: def input_example(self, max_batch=1, max_dim=768, seq_len=6):
      return [
          torch.randint(
              low=-3,
              high=3,
              size=(max_batch, seq_len, max_dim),
              device=self.device,
              dtype=torch.float32,
          )
      ]
  
imports: import random
  from typing import Dict, List, Optional
  import torch
  import torch.nn.functional
  from nemo.collections.nlp.modules.common.megatron.utils import build_position_ids
  from nemo.core.classes.exportable import Exportable
  from nemo.core.neural_types import ChannelType, EncodedRepresentation, MaskType, NeuralType
label_desc: Use image node_id 5 for calling the TokensHeadEmb obj's underlying member method code with example usage: obj.input_example(max_batch, max_dim, seq_len) and returns: list