class_docstr: The prompt encoder network that is used to generate the virtual 
  token embeddings for p-tuning.
docstr: Forward pass through the encoder with caching of prompt representations
docstr_tok: ["Forward","pass","through","the","encoder","with","caching","of","prompt","representations"]
code: def forward(
      self, batch_size: int, use_cached_reps: bool
  ) -> torch.Tensor:
      """
      Forward pass through the encoder with caching of prompt representations
      """
      if use_cached_reps:
          output_embeds = self.get_inference_table().unsqueeze(0)
      else:
          if self.training:
              if self.inference_table.is_inference_ready:
                  self.clear_inference_table()
              output_embeds = self._forward()
          else:
              if not self.inference_table.is_inference_ready:
                  output_embeds = self._forward()
                  self.set_inference_table(output_embeds.squeeze(0))
              output_embeds = self.get_inference_table().unsqueeze(0)
  
      output_embeds = output_embeds.expand(
          batch_size, self.total_virtual_tokens, self.token_dim
      )
      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 8 for calling the PromptEncoder obj's underlying member method code with example usage: obj.forward(batch_size, use_cached_reps) and returns: output_embeds