code: def sample_random_times(self, batch_size, *, device):
      return torch.rand(
          (batch_size,),
          device=device,
          generator=self.rng,
          dtype=torch.float32,
      )
  
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 3 for calling the GaussianDiffusionContinuousTimes obj's underlying member method code with example usage: obj.sample_random_times(batch_size) and returns: torch