code: def consistency_losses(
      self,
      model,
      x_start,
      num_scales,
      model_kwargs=None,
      target_model=None,
      teacher_model=None,
      teacher_diffusion=None,
      noise=None,
  ):
      if model_kwargs is None:
          model_kwargs = {}
      if noise is None:
          noise = th.randn_like(x_start)
  
      dims = x_start.ndim
  
      def denoise_fn(x, t):
          return self.denoise(model, x, t, **model_kwargs)[1]
  
      if target_model:
  
          @th.no_grad()
          def target_denoise_fn(x, t):
              return self.denoise(target_model, x, t, **model_kwargs)[1]
  
      else:
          raise NotImplementedError("Must have a target model")
  
      if teacher_model:
  
          @th.no_grad()
          def teacher_denoise_fn(x, t):
              return teacher_diffusion.denoise(
                  teacher_model, x, t, **model_kwargs
              )[1]
  
      @th.no_grad()
      def heun_solver(samples, t, next_t, x0):
          x = samples
          if teacher_model is None:
              denoiser = x0
          else:
              denoiser = teacher_denoise_fn(x, t)
  
          d = (x - denoiser) / append_dims(t, dims)
          samples = x + d * append_dims(next_t - t, dims)
          if teacher_model is None:
              denoiser = x0
          else:
              denoiser = teacher_denoise_fn(samples, next_t)
  
          next_d = (samples - denoiser) / append_dims(next_t, dims)
          samples = x + (d + next_d) * append_dims(
              (next_t - t) / 2, dims
          )
  
          return samples
  
      @th.no_grad()
      def euler_solver(samples, t, next_t, x0):
          x = samples
          if teacher_model is None:
              denoiser = x0
          else:
              denoiser = teacher_denoise_fn(x, t)
          d = (x - denoiser) / append_dims(t, dims)
          samples = x + d * append_dims(next_t - t, dims)
  
          return samples
  
      indices = th.randint(
          0, num_scales - 1, (x_start.shape[0],), device=x_start.device
      )
  
      t = self.sigma_max ** (1 / self.rho) + indices / (
          num_scales - 1
      ) * (
          self.sigma_min ** (1 / self.rho)
          - self.sigma_max ** (1 / self.rho)
      )
      t = t**self.rho
  
      t2 = self.sigma_max ** (1 / self.rho) + (indices + 1) / (
          num_scales - 1
      ) * (
          self.sigma_min ** (1 / self.rho)
          - self.sigma_max ** (1 / self.rho)
      )
      t2 = t2**self.rho
  
      x_t = x_start + noise * append_dims(t, dims)
  
      dropout_state = th.get_rng_state()
      distiller = denoise_fn(x_t, t)
  
      if teacher_model is None:
          x_t2 = euler_solver(x_t, t, t2, x_start).detach()
      else:
          x_t2 = heun_solver(x_t, t, t2, x_start).detach()
  
      th.set_rng_state(dropout_state)
      distiller_target = target_denoise_fn(x_t2, t2)
      distiller_target = distiller_target.detach()
  
      snrs = self.get_snr(t)
      weights = get_weightings(
          self.weight_schedule, snrs, self.sigma_data
      )
      if self.loss_norm == "l1":
          diffs = th.abs(distiller - distiller_target)
          loss = mean_flat(diffs) * weights
      elif self.loss_norm == "l2":
          diffs = (distiller - distiller_target) ** 2
          loss = mean_flat(diffs) * weights
      elif self.loss_norm == "l2-32":
          distiller = F.interpolate(distiller, size=32, mode="bilinear")
          distiller_target = F.interpolate(
              distiller_target,
              size=32,
              mode="bilinear",
          )
          diffs = (distiller - distiller_target) ** 2
          loss = mean_flat(diffs) * weights
      elif self.loss_norm == "lpips":
          if x_start.shape[-1] < 256:
              distiller = F.interpolate(
                  distiller, size=224, mode="bilinear"
              )
              distiller_target = F.interpolate(
                  distiller_target, size=224, mode="bilinear"
              )
  
          loss = (
              self.lpips_loss(
                  (distiller + 1) / 2.0,
                  (distiller_target + 1) / 2.0,
              )
              * weights
          )
      else:
          raise ValueError(f"Unknown loss norm {self.loss_norm}")
  
      terms = {}
      terms["loss"] = loss
  
      return terms
  
imports: import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from piq import LPIPS
  from torchvision.transforms import RandomCrop
  from .None import dist_util
  from .nn import mean_flat, append_dims, append_zero
  from .random_util import get_generator
label_desc: Use image node_id 7 for calling the KarrasDenoiser obj's underlying member method code with example usage: obj.consistency_losses(model, x_start, num_scales, model_kwargs, target_model, teacher_model, teacher_diffusion, noise) and returns: terms, self, samples, samples, self, teacher_diffusion