code: def clip_encode(self, text):
      batch_encoding = self.tokenizer(
          text,
          truncation=True,
          max_length=self.max_length,
          return_length=True,
          return_overflowing_tokens=False,
          padding="max_length",
          return_tensors="pt",
      )
      tokens = batch_encoding["input_ids"].to("cuda", non_blocking=True)
      z = self.text_encoder.infer(
          {"tokens": device_view(tokens.type(torch.int32))}
      )["logits"].clone()
      seq_len = (z.shape[1] + 8 - 1) // 8 * 8
      z = torch.nn.functional.pad(
          z, (0, 0, 0, seq_len - z.shape[1]), value=0.0
      )
      return z
  
imports: import logging
  import os
  import tempfile
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import OmegaConf
  from polygraphy import cuda
  from transformers import CLIPTokenizer
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion
  from nemo.collections.multimodal.modules.nerf.guidance.txt2img_guidance_base import Txt2ImgGuidanceBase
  from nemo.collections.multimodal.modules.nerf.utils.trt_engine import Engine, device_view
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import extract_into_tensor, make_beta_schedule
  from nemo.collections.multimodal.parts.stable_diffusion.utils import default
  from nemo.collections.multimodal.parts.utils import randn_like
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
label_desc: Use image node_id 5 for calling the LatentDiffusionWrapper obj's underlying member method code with example usage: obj.clip_encode(text) and returns: z