code: def test_generation_batched(self):
      model_id = "mosaicml/mpt-7b"
      tokenizer = AutoTokenizer.from_pretrained(model_id)
  
      # Load in 4bit to fit the daily CI runner GPU RAM
      model = MptForCausalLM.from_pretrained(
          model_id,
          torch_dtype=torch.bfloat16,
          device_map={"": 0},
          load_in_4bit=True,
      )
  
      input_texts = [
          "Hello my name is",
          "Today I am going at the gym and",
      ]
      tokenizer.pad_token_id = tokenizer.eos_token_id
      tokenizer.padding_side = "left"
  
      inputs = tokenizer(
          input_texts, return_tensors="pt", padding=True
      ).to(torch_device)
  
      expected_output = [
          "Hello my name is Tiffany and I am a mother of two beautiful children. I have been a nanny for over",
          "Today I am going at the gym and then I am going to go to the grocery store and get some food. I am going to make",
      ]
      outputs = model.generate(**inputs, max_new_tokens=20)
  
      decoded_outputs = tokenizer.batch_decode(
          outputs, skip_special_tokens=True
      )
      for i, predicted_output in enumerate(decoded_outputs):
          self.assertEqual(predicted_output, expected_output[i])
  
imports: import math
  import unittest
  from transformers import MptConfig, is_torch_available
  from transformers.testing_utils import require_bitsandbytes, require_torch, require_torch_gpu, 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 3 for calling the MptIntegrationTests obj's underlying member method code with example usage: obj.test_generation_batched() without return types