class_docstr: The prompt encoder network that is used to generate the virtual 
  token embeddings for p-tuning.
docstr: This method caches the output representation from the Encoder and saves it inside `self.inference_table`.
docstr_tok: ["This","method","caches","the","output","representation","from","the","Encoder","and","saves","it","inside","`","self.inference_table","`","."]
code: def set_inference_table(self, prompt_representation: torch.Tensor):
      """
      This method caches the output representation from the Encoder and saves it inside `self.inference_table`.
      """
      prompt_representation = prompt_representation.detach().clone()
      self.inference_table.set_prompt_table(prompt_representation)
  
imports: import copy
  import enum
  from typing import Dict, Optional
  import torch
  import torch.nn.init
  from torch import nn
  from nemo.collections.nlp.modules.common.megatron.fused_bias_gelu import fused_bias_gelu
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults, init_method_normal
  from nemo.core.classes import Exportable, NeuralModule
  from nemo.core.classes.common import typecheck
label_desc: Use image node_id 2 for calling the PromptEncoder obj's underlying member method code with example usage: obj.set_inference_table(prompt_representation) without return types