code: def get_times(self, batch_size, noise_level, *, device):
      return torch.full(
          (batch_size,), noise_level, device=device, 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 2 for calling the GaussianDiffusionContinuousTimes obj's underlying member method code with example usage: obj.get_times(batch_size, noise_level) and returns: torch