class_docstr: This is the implementation of `The Power of Scale for Parameter-Efficient
  Prompt Tuning <https://arxiv.org/pdf/2104.08691v1.pdf>`_ . Similar to :obj:`PrefixTuningTemplate`,
  This template also does not need any textual template. Addition tokens are directly
  concatenated into the input ids. There are two initializations of the new tokens.
  (1). random initialization. (2) initialize with the tokens of the plm (We simply take
  the first n_tokens similar to their implementation).
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code: def on_text_set(self):
      self.text = self.parse_text(self.text)
  
file_path: OpenPrompt/openprompt/prompts/soft_template.py
imports: import os
  from torch.nn.parameter import Parameter
  from openprompt.utils.logging import logger
  from openprompt.data_utils import InputExample, InputFeatures
  from typing import *
  from transformers import PreTrainedModel
  from transformers.tokenization_utils import PreTrainedTokenizer
  from openprompt import Template
  from openprompt.prompts import ManualTemplate, ManualVerbalizer
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the SoftTemplate obj's underlying member method code with example usage: obj.on_text_set() without return types