class_docstr: The prompt encoder network that is used to generate the virtual 
  token embeddings for p-tuning.
code: def state_dict(self, desination=None, prefix=None, keep_vars=False):
      _state_dict = {}
      _state_dict[
          "prompt_table"
      ] = (
          self.inference_table.state_dict()
      )  # (@adithyare) this key is for backward compatibility with downstream users of the "inference ready" model.
      _state_dict["embeddings"] = self.embedding.state_dict()
      if self.encoder_type == PromptEncoderType.EMBEDDING:
          pass
      elif self.encoder_type == PromptEncoderType.LSTM:
          _state_dict["mlp_head"] = self.mlp_head.state_dict()
          _state_dict["lstm_head"] = self.lstm_head.state_dict()
      elif self.encoder_type == PromptEncoderType.MLP:
          _state_dict["mlp_head"] = self.mlp_head.state_dict()
      elif self.encoder_type == PromptEncoderType.TPMLP:
          _state_dict["tpmlp"] = self.tpmlp.state_dict()
      else:
          raise ValueError("Prompt encoder type not recognized. Pl.")
      return _state_dict
  
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 5 for calling the PromptEncoder obj's underlying member method code with example usage: obj.state_dict(desination, prefix, keep_vars) and returns: _state_dict