docstr: Encode conditioning c.
  For txt2img use-case, the input conditioning would be the plain text,
  and output would be the encoded embedding for the corresponding text;
  For img2img use-case, the input conditioning would be the raw image,
  and output would be the corresponding image embedding
  
  Args:
      c: conditioning
  
  Returns:
      encoded conditioning
docstr_tok: ["Encode","conditioning","c.","For","txt2img","use-case",",","the","input","conditioning","would","be","the","plain","text",",","and","output","would","be","the","encoded","embedding","for","the","corresponding","text",";","For","img2img","use-case",",","the","input","conditioning","would","be","the","raw","image",",","and","output","would","be","the","corresponding","image","embedding","Args",":","c",":","conditioning","Returns",":","encoded","conditioning"]
code: def get_conditioning(self, c: Any) -> Any:
      """
      Encode conditioning c.
      For txt2img use-case, the input conditioning would be the plain text,
      and output would be the encoded embedding for the corresponding text;
      For img2img use-case, the input conditioning would be the raw image,
      and output would be the corresponding image embedding
  
      Args:
          c: conditioning
  
      Returns:
          encoded conditioning
      """
      pass
  
file_path: NeMo/nemo/collections/multimodal/models/text_to_image/stable_diffusion/diffusion_model.py
imports: import re
  from abc import ABC, abstractclassmethod
  from typing import Any, Optional
  import torch
  from nemo.core.classes import ModelPT
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the DiffusionModel obj's underlying member method code with example usage: obj.get_conditioning(c) without return types