class_docstr: The prompt encoder network that is used to generate the virtual 
  token embeddings for p-tuning.
code: def _forward(
      self,
  ):
      input_embeds = self.embedding(self.indices).unsqueeze(0)
      if self.encoder_type == PromptEncoderType.EMBEDDING:
          output_embeds = input_embeds
      elif self.encoder_type == PromptEncoderType.LSTM:
          output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0])
      elif self.encoder_type == PromptEncoderType.MLP:
          output_embeds = self.mlp_head(input_embeds)
      elif self.encoder_type == PromptEncoderType.TPMLP:
          output_embeds = self.tpmlp(input_embeds)
      else:
          raise ValueError("Prompt encoder type not recognized. Pl.")
      return output_embeds
  
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 7 for calling the PromptEncoder obj's underlying member method code with example usage: obj._forward() and returns: output_embeds