class_docstr: A sequential sampler used in FeatureSet when get (train=false) iterator .
code: def __init__(self, dataset, num_replicas, rank):
      self.dataset = dataset
      self.num_samples = int(
          math.floor(len(self.dataset) * 1.0 / num_replicas)
      )
      extra_samples = len(self.dataset) % num_replicas
      self.epoch = 0
      if extra_samples > rank:
          self.num_samples += 1
          self.offset = self.num_samples * rank
      else:
          self.offset = self.num_samples * rank + extra_samples
      self.total_size = len(dataset)
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/torch/utils.py
imports: from torch.utils.data.sampler import Sampler
  import math
label_desc: Use image node_id 1 to create a new DistributedSequentialSampler object from inherited base classes: Sampler with example: obj = DistributedSequentialSampler(dataset, num_replicas, rank)