docstr: Return the data prediction model (with thresholding).
docstr_tok: ["Return","the","data","prediction","model","(","with","thresholding",")","."]
code: def data_prediction_fn(self, x, t):
      """
      Return the data prediction model (with thresholding).
      """
      noise = self.noise_prediction_fn(x, t)
      dims = x.dim()
      alpha_t, sigma_t = (
          self.noise_schedule.marginal_alpha(t),
          self.noise_schedule.marginal_std(t),
      )
      x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(
          alpha_t, dims
      )
      if self.thresholding:
          p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
          s = torch.quantile(
              torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1
          )
          s = expand_dims(
              torch.maximum(
                  s, self.max_val * torch.ones_like(s).to(s.device)
              ),
              dims,
          )
          x0 = torch.clamp(x0, -s, s) / s
      return x0
  
imports: import math
  import torch
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import expand_dims, interpolate_fn
label_desc: Use image node_id 3 for calling the DPMSolver obj's underlying member method code with example usage: obj.data_prediction_fn(x, t) and returns: x0