code: def dynamic_quant_filter_fn(mod, *args):
      return (
          isinstance(mod, torch.nn.Linear)
          and mod.in_features > 16
          and (mod.in_features, mod.out_features)
          not in [
              (1280, 640),
              (1920, 1280),
              (1920, 640),
              (2048, 1280),
              (2048, 2560),
              (2560, 1280),
              (256, 128),
              (2816, 1280),
              (320, 640),
              (512, 1536),
              (512, 256),
              (512, 512),
              (640, 1280),
              (640, 1920),
              (640, 320),
              (640, 5120),
              (640, 640),
              (960, 320),
              (960, 640),
          ]
      )
  
imports: import torch
  from torchao.quantization import apply_dynamic_quant, change_linear_weights_to_int4_woqtensors, change_linear_weights_to_int8_woqtensors, swap_conv2d_1x1_to_linear
  from diffusers import AutoencoderKL, DiffusionPipeline, DPMSolverMultistepScheduler
label_desc: Use image node_id 1 for calling a global function with example usage: dynamic_quant_filter_fn(mod) and returns: unknown