code: def retrieve_latents(
      encoder_output: torch.Tensor,
      generator: Optional[torch.Generator] = None,
      sample_mode: str = "sample",
  ):
      if (
          hasattr(encoder_output, "latent_dist")
          and sample_mode == "sample"
      ):
          return encoder_output.latent_dist.sample(generator)
      elif (
          hasattr(encoder_output, "latent_dist")
          and sample_mode == "argmax"
      ):
          return encoder_output.latent_dist.mode()
      elif hasattr(encoder_output, "latents"):
          return encoder_output.latents
      else:
          raise AttributeError(
              "Could not access latents of provided encoder_output"
          )
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  from diffusers.utils.import_utils import is_invisible_watermark_available
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import IPAdapterMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel
  from ...models.attention_processor import AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import is_compiled_module, randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
  from .multicontrolnet import MultiControlNetModel
label_desc: Use image node_id 1 for calling a global function with example usage: retrieve_latents(encoder_output, generator, sample_mode) and returns: encoder_output, encoder_output, encoder_output