class_docstr: The prompt encoder network that is used to generate the virtual 
  token embeddings for p-tuning.
code: def load_state_dict(self, state_dict, strict=True):
      self.inference_table.load_state_dict(state_dict["prompt_table"])
      self.embedding.load_state_dict(state_dict["embeddings"])
      if self.encoder_type == PromptEncoderType.EMBEDDING:
          pass
      elif self.encoder_type == PromptEncoderType.LSTM:
          self.mlp_head.load_state_dict(state_dict["mlp_head"])
          self.lstm_head.state_dict(state_dict["lstm_head"])
      elif self.encoder_type == PromptEncoderType.MLP:
          self.mlp_head.load_state_dict(state_dict["mlp_head"])
      elif self.encoder_type == PromptEncoderType.TPMLP:
          self.tpmlp.load_state_dict(state_dict["tpmlp"])
      else:
          raise ValueError("Prompt encoder type not recognized. Pl.")
      return
  
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 6 for calling the PromptEncoder obj's underlying member method code with example usage: obj.load_state_dict(state_dict, strict) without return types