code: def pad_cond(tensor, repeats, empty):
      if not isinstance(tensor, dict):
          return torch.cat(
              [tensor, empty.repeat((tensor.shape[0], repeats, 1))],
              axis=1,
          )
  
      tensor["crossattn"] = pad_cond(
          tensor["crossattn"], repeats, empty
      )
      return tensor
  
file_path: stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py
imports: import torch
  from modules import prompt_parser, devices, sd_samplers_common
  from modules.shared import opts, state
  import modules.shared
  from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
  from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
  from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
label_desc: Use image node_id 3 for calling a global function with example usage: pad_cond(tensor, repeats, empty) and returns: tensor, torch