code: def test(self, num_ranks, batch, micro_batch, gas, success):
      assert (
          dist.get_world_size() == num_ranks
      ), "The test assumes a world size of f{num_ranks}"
  
      ds_batch_config = get_test_path("ds_batch_config.json")
      ds_config = DeepSpeedConfig(ds_batch_config)
  
      # test cases when all parameters are provided
      status = _run_batch_config(
          ds_config, train_batch=batch, micro_batch=micro_batch, gas=gas
      )
      _batch_assert(status, ds_config, batch, micro_batch, gas, success)
  
      # test cases when two out of three parameters are provided
      status = _run_batch_config(
          ds_config, train_batch=batch, micro_batch=micro_batch
      )
      _batch_assert(status, ds_config, batch, micro_batch, gas, success)
  
      if success:
          # when gas is provided with one more parameter
          status = _run_batch_config(
              ds_config, train_batch=batch, gas=gas
          )
          _batch_assert(
              status, ds_config, batch, micro_batch, gas, success
          )
  
          status = _run_batch_config(
              ds_config, micro_batch=micro_batch, gas=gas
          )
          _batch_assert(
              status, ds_config, batch, micro_batch, gas, success
          )
  
          # test the case when only micro_batch or train_batch is provided
          if gas == 1:
              status = _run_batch_config(
                  ds_config, micro_batch=micro_batch
              )
              _batch_assert(
                  status, ds_config, batch, micro_batch, gas, success
              )
  
              status = _run_batch_config(ds_config, train_batch=batch)
              _batch_assert(
                  status, ds_config, batch, micro_batch, gas, success
              )
      else:
          # when only gas is provided
          status = _run_batch_config(ds_config, gas=gas)
          _batch_assert(
              status, ds_config, batch, micro_batch, gas, success
          )
  
          # when gas is provided with something else and gas does not divide batch
          if gas != 1:
              status = _run_batch_config(
                  ds_config, train_batch=batch, gas=gas
              )
              _batch_assert(
                  status, ds_config, batch, micro_batch, gas, success
              )
  
imports: import torch
  import pytest
  import json
  import argparse
  from deepspeed.runtime.zero.config import DeepSpeedZeroConfig
  from unit.common import DistributedTest, get_test_path
  from unit.simple_model import SimpleModel, create_config_from_dict, random_dataloader
  import deepspeed.comm
  import deepspeed
  from deepspeed.runtime.config import DeepSpeedConfig, get_bfloat16_enabled
label_desc: Use image node_id 1 for calling the TestBatchConfig obj's underlying member method code with example usage: obj.test(num_ranks, batch, micro_batch, gas, success) without return types