code: def __getitem__(self, i):  # Directly get a batch
      # print("Loading batch ", i)
      if i == 0 and self.current_index != 0:
          self.reset()
      current_available_size = self.current_offset - i * self.batch_size
      x_list = []
      y_list = []
      if current_available_size < self.batch_size:
          if current_available_size != 0:
              # Add all the remaining records into this batch
              x_list.append(
                  index(self.current_x, start=-current_available_size)
              )
              y_list.append(
                  index(self.current_y, start=-current_available_size)
              )
          # Load subsequent file(s) to complete the batch
          remain_size = self.batch_size - current_available_size
          while True:
              self.current_index += 1
              self.load_from_plasma(self.current_index)
              if self.sizes[self.current_index] >= remain_size:
                  x_list.append(index(self.current_x, end=remain_size))
                  y_list.append(index(self.current_y, end=remain_size))
                  break
              else:
                  x_list.append(self.current_x)
                  y_list.append(self.current_y)
                  remain_size -= self.sizes[self.current_index]
                  if remain_size == 0:
                      break
      # The current file contains a full batch
      elif current_available_size == self.batch_size:
          x_list.append(
              index(self.current_x, start=-current_available_size)
          )
          y_list.append(
              index(self.current_y, start=-current_available_size)
          )
      else:
          x_list.append(
              index(
                  self.current_x,
                  start=-current_available_size,
                  end=-current_available_size + self.batch_size,
              )
          )
          y_list.append(
              index(
                  self.current_y,
                  start=-current_available_size,
                  end=-current_available_size + self.batch_size,
              )
          )
  
      if isinstance(self.current_x, list):
          x_np = []
          for i in range(len(self.current_x)):
              x_np.append(np.concatenate([x[i] for x in x_list]))
      else:
          x_np = np.concatenate(x_list)
      y_np = np.concatenate(y_list)
      # Can put collate_fn into train_func if necessary.
      return x_np, y_np
  
imports: import os
  import subprocess
  import types
  import cloudpickle
  from pyspark.rdd import RDD
  from pyspark.sql import DataFrame
  from torch.utils.data import Dataset, DataLoader
  from bigdl.dllib.utils.log4Error import *
  from bigdl.dllib.utils.utils import get_node_ip
  from bigdl.orca.learn.mpi.mpi_runner import MPIRunner
  from bigdl.orca.learn.mpi.utils import *
label_desc: Use image node_id 5 for calling the PlasmaNDArrayDataset obj's underlying member method code with example usage: obj.__getitem__(i) and returns: x_np,  y_np