code: def sample(self, bs, device):
      if self.even:
          # buckets = [1/G]
          start_i, end_i = self.rank * bs, (self.rank + 1) * bs
          global_batch_size = self.size * bs
          locs = (
              th.arange(start_i, end_i) + th.rand(bs)
          ) / global_batch_size
          log_sigmas = th.tensor(
              self.inv_cdf(locs), dtype=th.float32, device=device
          )
      else:
          log_sigmas = self.p_mean + self.p_std * th.randn(
              bs, device=device
          )
      sigmas = th.exp(log_sigmas)
      weights = th.ones_like(sigmas)
      return sigmas, weights
  
imports: from abc import ABC, abstractmethod
  import numpy
  import torch
  from scipy.stats import norm
  import torch.distributed
label_desc: Use image node_id 2 for calling the LogNormalSampler obj's underlying member method code with example usage: obj.sample(bs, device) and returns: sigmas,  weights