code: def q_sample(self, x_start, t, noise=None):
      dtype = x_start.dtype
  
      if isinstance(t, float):
          batch = x_start.shape[0]
          t = torch.full(
              (batch,), t, device=x_start.device, dtype=dtype
          )
  
      noise = default(
          noise, lambda: randn_like(x_start, generator=self.rng)
      )
      log_snr = self.log_snr(t).type(dtype)
      log_snr_padded_dim = right_pad_dims_to(x_start, log_snr)
      alpha, sigma = log_snr_to_alpha_sigma(log_snr_padded_dim)
  
      return alpha * x_start + sigma * noise, log_snr, alpha, sigma
  
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 7 for calling the GaussianDiffusionContinuousTimes obj's underlying member method code with example usage: obj.q_sample(x_start, t, noise) and returns: unknown,  log_snr,  alpha,  sigma