docstr: Get text embeddings from the given prompt.
  
  Parameters:
      prompt (str): Input text.
  
  Returns:
      torch.Tensor: Text embeddings tensor [B, 77, 1024].
docstr_tok: ["Get","text","embeddings","from","the","given","prompt",".","Parameters",":","prompt","(","str",")",":","Input","text",".","Returns",":","torch.Tensor",":","Text","embeddings","tensor","[","B",",","77",",","1024","]","."]
code: def get_text_embeds(self, prompt: str) -> torch.Tensor:
      """
      Get text embeddings from the given prompt.
  
      Parameters:
          prompt (str): Input text.
  
      Returns:
          torch.Tensor: Text embeddings tensor [B, 77, 1024].
      """
      inputs = self.tokenizer(
          prompt,
          padding="max_length",
          max_length=self.tokenizer.model_max_length,
          return_tensors="pt",
      )
      embeddings = self.text_encoder(inputs.input_ids.to(self.device))[
          0
      ]
      return embeddings
  
file_path: NeMo/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_huggingface_pipeline.py
imports: from typing import List
  import torch
  import torch.nn.functional
  from diffusers import DDIMScheduler, StableDiffusionPipeline
  from nemo.collections.multimodal.modules.nerf.guidance.txt2img_guidance_base import Txt2ImgGuidanceBase
label_desc: Use image node_id 3 for calling the StableDiffusion obj's underlying member method code with example usage: obj.get_text_embeds(prompt) and returns: embeddings