class_docstr: ``PromptModel`` is the encapsulation of ``Template`` and the ``pre-trained model``,
  with OpenPrompt, these modules could be flexibly combined. And this class is the base class of ``PromptForClassification`` and ``PromptForGeneration``
  
  Args:
      plm (:obj:`PreTrainedModel`): The pre-trained language model for the current prompt-learning task.
      template (:obj:`Template`): The ``Template`` object to warp the input data.
      freeze_plm (:obj:`bool`): whether or not to freeze the pretrained language model
      plm_eval_mode (:obj:`bool`): this is a stronger freezing mode than freeze_plm, i.e. the dropout of the model is turned off. No matter whether the other part is set to train.
docstr: This is a forward method to make wrapped input data go through the model, and return the output logits.
  Typically, this function aims to predict the ``<mask>`` position.
  
  Args:
      batch (:obj:`Union[Dict, InputFeatures]`): The input features of batchified data sequences.
docstr_tok: ["This","is","a","forward","method","to","make","wrapped","input","data","go","through","the","model",",","and","return","the","output","logits",".","Typically",",","this","function","aims","to","predict","the","``","<","mask",">","``","position",".","Args",":","batch","(",":","obj",":","`","Union","[","Dict",",","InputFeatures","]","`",")",":","The","input","features","of","batchified","data","sequences","."]
code: def forward(self, batch):
      r"""
      This is a forward method to make wrapped input data go through the model, and return the output logits.
      Typically, this function aims to predict the ``<mask>`` position.
  
      Args:
          batch (:obj:`Union[Dict, InputFeatures]`): The input features of batchified data sequences.
      """
      batch = self.template.process_batch(batch)
      input_batch = {
          key: batch[key] for key in batch if key in self.forward_keys
      }
      outputs = self.plm(**input_batch, output_hidden_states=True)
      outputs = self.template.post_processing_outputs(outputs)
      return outputs
  
imports: from typing import *
  from utils import round_list, signature
  import paddle
label_desc: Use image node_id 4 for calling the erniePromptModel obj's underlying member method code with example usage: obj.forward(batch) and returns: outputs