code: def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
      """
      Returns:
          a state dict containing just the attention processor parameters.
      """
      attn_processors = unet.attn_processors
  
      attn_processors_state_dict = {}
  
      for attn_processor_key, attn_processor in attn_processors.items():
          for (
              parameter_key,
              parameter,
          ) in attn_processor.state_dict().items():
              param_name = f"{attn_processor_key}.{parameter_key}"
              attn_processors_state_dict[param_name] = parameter
      return attn_processors_state_dict
  
imports: from typing import Dict
  import itertools
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
  from diffusers.loaders import LoraLoaderMixin, text_encoder_lora_state_dict
  from diffusers.models.attention_processor import AttnAddedKVProcessor, AttnAddedKVProcessor2_0, LoRAAttnAddedKVProcessor, LoRAAttnProcessor, LoRAAttnProcessor2_0, SlicedAttnAddedKVProcessor
  from diffusers.utils.import_utils import is_xformers_available
  from ray.train import ScalingConfig
  from ray import train
  from ray.train.torch import TorchTrainer
  import torch
  import torch.nn.functional
  from torch.nn.utils import clip_grad_norm_
  from transformers import CLIPTextModel
  from dataset import collate, get_train_dataset
  from flags import train_arguments
label_desc: Use image node_id 6 for calling a global function with example usage: unet_attn_processors_state_dict(unet) and returns: attn_processors_state_dict