class_docstr: The Tensor Parallel MLP prompt encoder network that is used to generate the virtual 
  token embeddings for p-tuning. It only have two layers.
code: def forward(self, input_embeds) -> torch.Tensor:
      intermediate_parallel, bias_parallel = self.first(input_embeds)
      intermediate_parallel = fused_bias_gelu(
          intermediate_parallel, bias_parallel
      )
      output_embeds, bias_parallel = self.second(intermediate_parallel)
      output_embeds = output_embeds + bias_parallel
      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 2 for calling the TPMLP obj's underlying member method code with example usage: obj.forward(input_embeds) and returns: output_embeds