code: def __call__(self):
      image = torch.randn(
          (
              1,
              self.unet.config.in_channels,
              self.unet.config.sample_size,
              self.unet.config.sample_size,
          ),
      )
      timestep = 1
  
      model_output = self.unet(image, timestep).sample
      scheduler_output = self.scheduler.step(
          model_output, timestep, image
      ).prev_sample
  
      result = (
          scheduler_output
          - scheduler_output
          + torch.ones_like(scheduler_output)
      )
  
      return result
  
imports: import torch
  from diffusers import DiffusionPipeline
label_desc: Use image node_id 2 for calling the UnetSchedulerOneForwardPipeline obj's underlying member method code with example usage: obj.__call__() and returns: result