code: def test_general_uni100_dataloader_with_batch_size_in_101(self):
      train_batch_size = 6
      eval_batch_size = 101
      config_dict = {
          "model": "BPR",
          "dataset": "general_uni100_dataloader",
          "data_path": current_path,
          "load_col": None,
          "train_neg_sample_args": {
              "distribution": "uniform",
              "sample_num": 1,
          },
          "eval_args": {
              "split": {"RS": [0.8, 0.1, 0.1]},
              "order": "TO",
              "mode": "uni100",
          },
          "train_batch_size": train_batch_size,
          "eval_batch_size": eval_batch_size,
      }
      train_data, valid_data, test_data = new_dataloader(
          config_dict=config_dict
      )
  
      def check_result(data, result):
          assert data._batch_size == 202
          assert len(data) == len(result)
          for i, batch_data in enumerate(data):
              user_df, row_idx, positive_u, positive_i = batch_data
              assert result[i]["item_id_check"](user_df["item_id"])
              assert (row_idx.numpy() == result[i]["row_idx"]).all()
              assert (
                  positive_u.numpy() == result[i]["positive_u"]
              ).all()
              assert (
                  positive_i.numpy() == result[i]["positive_i"]
              ).all()
  
      valid_result = [
          {
              "item_id_check": lambda data: data[0] == 9
              and (8 < data[1:]).all()
              and (data[1:] <= 100).all(),
              "row_idx": [0] * 101,
              "positive_u": [0],
              "positive_i": [9],
          },
          {
              "item_id_check": lambda data: data[0] == 1
              and (data[1:] != 1).all(),
              "row_idx": [0] * 101,
              "positive_u": [0],
              "positive_i": [1],
          },
          {
              "item_id_check": lambda data: (
                  data[0:2].numpy() == [17, 18]
              ).all()
              and (16 < data[2:]).all()
              and (data[2:] <= 100).all(),
              "row_idx": [0] * 202,
              "positive_u": [0, 0],
              "positive_i": [17, 18],
          },
      ]
      check_result(valid_data, valid_result)
  
      test_result = [
          {
              "item_id_check": lambda data: data[0] == 10
              and (9 < data[1:]).all()
              and (data[1:] <= 100).all(),
              "row_idx": [0] * 101,
              "positive_u": [0],
              "positive_i": [10],
          },
          {
              "item_id_check": lambda data: data[0] == 1
              and (data[1:] != 1).all(),
              "row_idx": [0] * 101,
              "positive_u": [0],
              "positive_i": [1],
          },
          {
              "item_id_check": lambda data: (
                  data[0:2].numpy() == [19, 20]
              ).all()
              and (18 < data[2:]).all()
              and (data[2:] <= 100).all(),
              "row_idx": [0] * 202,
              "positive_u": [0, 0],
              "positive_i": [19, 20],
          },
      ]
      check_result(test_data, test_result)
  
file_path: RecBole/tests/data/test_dataloader.py
imports: import logging
  import os
  import pytest
  from recbole.data.dataloader.general_dataloader import NegSampleEvalDataLoader, FullSortEvalDataLoader
  from recbole.config import Config
  from recbole.data import create_dataset, data_preparation
  from recbole.utils import init_seed
label_desc: Use image node_id 5 for calling the TestGeneralDataloader obj's underlying member method code with example usage: obj.test_general_uni100_dataloader_with_batch_size_in_101() without return types