code: def test_generate_merged_ids_with_target(
      tokenizer, input_ids, target_ids
  ):
      # Test case when target_ids is not None
      merged_ids, attention_masks = generate_merged_ids(
          input_ids, target_ids, tokenizer
      )
      assert torch.equal(
          merged_ids,
          torch.tensor(
              [[3, 4, 5, 9, 10, 11, 1], [6, 7, 8, 12, 13, 14, 1]]
          ),
      )
      assert merged_ids.shape == (2, 7)  # Check the shape of merged_ids
      assert attention_masks.shape == (2, 7)
  
imports: import pytest
  import torch
  from transformers import AutoConfig, AutoModelForCausalLM
  from ludwig.constants import LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.modules.training_hooks import NEFTuneHook
  from ludwig.utils.llm_utils import add_left_padding, create_attention_mask, FALLBACK_CONTEXT_LEN, find_last_matching_index, generate_merged_ids, get_context_len, get_realigned_target_and_prediction_tensors_for_inference, has_padding_token, pad_target_tensor_for_fine_tuning, remove_left_padding
  from ludwig.utils.tokenizers import HFTokenizer
label_desc: Use image node_id 11 for calling a global function with example usage: test_generate_merged_ids_with_target(tokenizer, input_ids, target_ids) without return types