code: def get_sampling_timesteps(self, batch, *, device):
      times = torch.linspace(
          1.0, 0.0, self.num_timesteps + 1, device=device
      )
      times = repeat(times, "t -> b t", b=batch)
      times = torch.stack((times[:, :-1], times[:, 1:]), dim=0)
      times = times.unbind(dim=-1)
      return times
  
imports: import math
  from functools import partial, wraps
  import torch
  import torch.nn
  from einops import repeat
  from torch.special import expm1
  from nemo.collections.multimodal.parts.utils import randn_like
label_desc: Use image node_id 5 for calling the GaussianDiffusionContinuousTimes obj's underlying member method code with example usage: obj.get_sampling_timesteps(batch) and returns: times