code: def create_and_check_forward_and_backwards(
      self,
      config,
      input_ids,
      input_mask,
      *args,
      gradient_checkpointing=False,
  ):
      model = BloomForCausalLM(config)
      model.to(torch_device)
      if gradient_checkpointing:
          model.gradient_checkpointing_enable()
  
      result = model(input_ids, labels=input_ids)
      self.parent.assertEqual(result.loss.shape, ())
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, self.seq_length, self.vocab_size),
      )
      result.loss.backward()
  
imports: import math
  import unittest
  from transformers import BloomConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 13 for calling the BloomModelTester obj's underlying member method code with example usage: obj.create_and_check_forward_and_backwards(config, input_ids, input_mask) without return types