class_docstr: Dataset loader for Libsvm data format 
class_docstr_tok: ["Dataset","loader","for","Libsvm","data","format"]
code: def pre_processing(mini_batch_data: List[Dict]):
      sample_lines = len(mini_batch_data)
      nfields = len(mini_batch_data[0].keys()) - 1
      feat_id = torch.LongTensor(sample_lines, nfields)
      feat_value = torch.FloatTensor(sample_lines, nfields)
      y = torch.FloatTensor(sample_lines)
  
      for i in range(sample_lines):
          row_value = mini_batch_data[i].values()
          sample = LibsvmDataset.decode_libsvm(list(row_value))
          feat_id[i] = sample["id"]
          feat_value[i] = sample["value"]
          y[i] = sample["y"]
      return feat_id, feat_value, y, sample_lines
  
imports: import calendar
  import os
  import time
  import requests
  import json
  from typing import List, Dict
  import traceback
  import orjson
  from argparse import Namespace
  from shared_config import parse_config_arguments
  from torch.utils.data import Dataset
  import torch
label_desc: Use image node_id 2 for calling the LibsvmDataset obj's underlying member method code with example usage: obj.pre_processing(mini_batch_data) and returns: feat_id,  feat_value,  y,  sample_lines