docstr: Construct a sequence object from HDF5 with required interface.
  
  Parameters
  ----------
  hdf_dataset : h5py.Dataset
      Dataset in HDF5 file.
  batch_size : int
      Size of a batch. When reading data to construct lightgbm Dataset, each read reads batch_size rows.
docstr_tok: ["Construct","a","sequence","object","from","HDF5","with","required","interface",".","Parameters","--","--","--","--","--","hdf_dataset",":","h5py.Dataset","Dataset","in","HDF5","file",".","batch_size",":","int","Size","of","a","batch",".","When","reading","data","to","construct","lightgbm","Dataset",",","each","read","reads","batch_size","rows","."]
code: def __init__(self, hdf_dataset, batch_size):
      """
      Construct a sequence object from HDF5 with required interface.
  
      Parameters
      ----------
      hdf_dataset : h5py.Dataset
          Dataset in HDF5 file.
      batch_size : int
          Size of a batch. When reading data to construct lightgbm Dataset, each read reads batch_size rows.
      """
      # We can also open HDF5 file once and get access to
      self.data = hdf_dataset
      self.batch_size = batch_size
  
file_path: LightGBM/examples/python-guide/dataset_from_multi_hdf5.py
imports: from pathlib import Path
  import h5py
  import numpy
  import pandas
  import lightgbm
label_desc: Use image node_id 1 to create a new HDFSequence object from inherited base classes: lgb with example: obj = HDFSequence(hdf_dataset, batch_size)