class_docstr: Base class for all the templates.
  Most of methods are abstract, with some exceptions to hold the common methods for all template, such as ``loss_ids``, ``save``, ``load``.
  
  Args:
      tokenizer (:obj:`PreTrainedTokenizer`): A tokenizer to appoint the vocabulary and the tokenization strategy.
      placeholder_mapping (:obj:`dict`): A place holder to represent the original input text.
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docstr: Get the loss indices for the template using mask.
  e.g. when self.text is ``'{"placeholder": "text_a"}. {"meta": "word"} is {"mask"}.'``,
  output is ``[0, 0, 0, 0, 1, 0]``.
  
  Returns:
      :obj:`List[int]`: A list of integers in the range [0, 1]:
  
      - 1 for a masked tokens.
      - 0 for a sequence tokens.
code: def get_default_loss_ids(self) -> List[int]:
      """Get the loss indices for the template using mask.
      e.g. when self.text is ``'{"placeholder": "text_a"}. {"meta": "word"} is {"mask"}.'``,
      output is ``[0, 0, 0, 0, 1, 0]``.
  
      Returns:
          :obj:`List[int]`: A list of integers in the range [0, 1]:
  
          - 1 for a masked tokens.
          - 0 for a sequence tokens.
      """
      return [1 if "mask" in d else 0 for d in self.text]
  
imports: from abc import abstractmethod
  import json
  from transformers.file_utils import ModelOutput
  from openprompt.config import convert_cfg_to_dict
  from transformers.utils.dummy_pt_objects import PreTrainedModel
  from openprompt.utils.utils import signature
  from yacs.config import CfgNode
  from openprompt.data_utils import InputFeatures, InputExample
  import torch
  import torch.nn
  from typing import *
  from transformers.tokenization_utils import PreTrainedTokenizer
  from openprompt.utils.logging import logger
  import numpy
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Template obj's underlying member method code with example usage: obj.get_default_loss_ids() and returns: unknown