code: def data_gen_for_conditional_generation():
      # labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
      #         Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
      #         or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
      #         only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
      data = data_gen()
      data["labels"] = torch.tensor([[0, 1]], dtype=torch.int64)
      return data
  
imports: import torch
  import transformers
  from ..registry import ModelAttribute, model_zoo
label_desc: Use image node_id 2 for calling a global function with example usage: data_gen_for_conditional_generation() and returns: data