code: def thresholding_x0(x0, method="dynamic", th=0.995):
      if method is None:
          return x0
      elif method == "static":
          return x0.clamp(-1.0, 1.0)
      elif method == "dynamic":
          # torch.quantile only suppoprt either float or double dtype
          # we need to manual cast it if running in FP16/AMP mode
          original_dtype = x0.dtype
          if original_dtype not in [torch.float, torch.double]:
              x0 = x0.float()
          s = torch.quantile(
              rearrange(x0, "b ... -> b (...)").abs(), th, dim=-1
          )  # From Figure A.10 (b)
          s.clamp_(min=1.0)
          s = right_pad_dims_to(x0, s)
          x0 = x0.clamp(-s, s) / s
          return x0.type(original_dtype)
      else:
          raise RuntimeError(
              f"Thresholding method: {method} not supported."
          )
  
imports: import numpy
  import torch
  from einops import rearrange
  from tqdm import tqdm
  from nemo.collections.multimodal.modules.imagen.sampler.batch_ops import batch_div, batch_mul
  from nemo.collections.multimodal.modules.imagen.sampler.continuous_ddpm import GaussianDiffusionContinuousTimes
label_desc: Use image node_id 2 for calling a global function with example usage: thresholding_x0(x0, method, th) and returns: x0, x0, x0