code: def split_attentions(*, weight, bias, split, chunk_size):
      weights = [None] * split
      biases = [None] * split
  
      weights_biases_idx = 0
  
      for starting_row_index in range(0, weight.shape[0], chunk_size):
          row_indices = torch.arange(
              starting_row_index, starting_row_index + chunk_size
          )
  
          weight_rows = weight[row_indices, :]
          bias_rows = bias[row_indices]
  
          if weights[weights_biases_idx] is None:
              weights[weights_biases_idx] = weight_rows
              biases[weights_biases_idx] = bias_rows
          else:
              assert weights[weights_biases_idx] is not None
              weights[weights_biases_idx] = torch.concat(
                  [weights[weights_biases_idx], weight_rows]
              )
              biases[weights_biases_idx] = torch.concat(
                  [biases[weights_biases_idx], bias_rows]
              )
  
          weights_biases_idx = (weights_biases_idx + 1) % split
  
      return weights, biases
  
imports: import argparse
  import inspect
  import os
  import numpy
  import torch
  from torch.nn import functional
  from transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer
  from diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet2DConditionModel
  from diffusers.pipelines.deepfloyd_if.safety_checker import IFSafetyChecker
label_desc: Use image node_id 14 for calling a global function with example usage: split_attentions() and returns: weights,  biases