class_docstr: A wrapper class that holds the output representations of the PromptEncoder Model. 
  At inference time we do not need to forward pass through the full PromptEncoder and can just use this class.
docstr: Method sets the prompt embedding inside self.prompt_table[taskname] with new weights
docstr_tok: ["Method","sets","the","prompt","embedding","inside","self.prompt_table","[","taskname","]","with","new","weights"]
code: def set_prompt_table(self, prompt_representation: torch.Tensor):
      """
      Method sets the prompt embedding inside self.prompt_table[taskname] with new weights
      """
      self.prompt_table[self.taskname].set_prompt_embedding_weights(
          prompt_representation
      )
      self.is_inference_ready = True
  
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 InferenceTable obj's underlying member method code with example usage: obj.set_prompt_table(prompt_representation) without return types