code: def prepare_config_and_inputs(self, gradient_checkpointing=False):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
  
      input_mask = None
      if self.use_input_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.seq_length]
          )
  
      sequence_labels = None
      if self.use_labels:
          sequence_labels = ids_tensor(
              [self.batch_size], self.type_sequence_label_size
          )
  
      config = self.get_config(
          gradient_checkpointing=gradient_checkpointing
      )
  
      return (config, input_ids, input_mask, sequence_labels)
  
imports: import math
  import unittest
  from transformers import BloomConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 3 for calling the BloomModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs(gradient_checkpointing) and returns: config,  input_ids,  input_mask,  sequence_labels