code: def get_pseudo_tokens(num_virtual_tokens):
      """
      Takes in an integer and returns a list of strings where each string
      is a numbered virtual token placeholder. If
      num_virtual_tokens = 3, then this function returns:
  
      ["<prompt_0>", "<prompt_1>", "<prompt_2>"]
  
      Args:
          num_virtual_tokens: (int) Number of virtual token strings you want to make
  
      returns a list of string.
  
      """
      pseudo_tokens = [
          VirtualPromptPlaceholderToken.BASE.value
          + str(i)
          + VirtualPromptPlaceholderToken.END.value
          for i in range(num_virtual_tokens)
      ]
  
      return pseudo_tokens
  
imports: import itertools
  import re
  from collections import OrderedDict
  from typing import Any, Optional
  import torch
  from omegaconf.dictconfig import DictConfig
  from omegaconf.omegaconf import open_dict
  from pytorch_lightning.trainer.trainer import Trainer
  from torch import Tensor
  from nemo.collections.common.tokenizers.sentencepiece_tokenizer import SentencePieceTokenizer
  from nemo.collections.nlp.metrics.prompt_learning_metrics import AccuracyScore, BLEUScore, ROUGEScores
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common import PromptEncoder, PromptEncoderType, VirtualPromptPlaceholderToken, VirtualPromptSource, VirtualPromptStyle
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults
  from nemo.collections.nlp.modules.common.transformer.text_generation import TextGeneration
  from nemo.collections.nlp.parts.nlp_overrides import GradScaler
  from nemo.utils import AppState, logging
label_desc: Use image node_id 1 for calling a global function with example usage: get_pseudo_tokens(num_virtual_tokens) and returns: pseudo_tokens