code: def test_generate_merged_ids_padding_removal(
      tokenizer, input_ids, target_ids
  ):
      # Test case to check removal of left padding from inputs and targets during merge
      padding_tokens = torch.tensor(
          [tokenizer.pad_token_id, tokenizer.pad_token_id]
      )
  
      # Adds 2 padding tokens to the left of input_ids and target_ids individually. Typically, if we just merged this
      # naively, we would expect to see [1, 1, 3, 4, 5, 1, 1, 9, 10, 11, 1], but we shouldn't see the padding tokens
      # except for the padding token at the end.
      input_ids_with_padding = torch.cat(
          (
              padding_tokens.unsqueeze(0).expand(input_ids.size(0), -1),
              input_ids,
          ),
          dim=1,
      )
      target_ids_with_padding = torch.cat(
          (
              padding_tokens.unsqueeze(0).expand(
                  target_ids.size(0), -1
              ),
              target_ids,
          ),
          dim=1,
      )
  
      merged_ids, attention_masks = generate_merged_ids(
          input_ids_with_padding, target_ids_with_padding, tokenizer
      )
  
      assert merged_ids.shape == (2, 7)  # Check the shape of merged_ids
      assert attention_masks.shape == (
          2,
          7,
      )  # Check the shape of attention_masks
  
      assert torch.equal(
          merged_ids[0][:3], input_ids[0]
      )  # Check the input_ids part without padding
      assert torch.equal(
          merged_ids[0][3:-1], target_ids[0]
      )  # Check the target_ids part without padding
      assert torch.equal(
          merged_ids[0][-1], torch.tensor(tokenizer.pad_token_id)
      )  # Check the padding tokens
  
      assert torch.all(attention_masks == 1)
  
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 13 for calling a global function with example usage: test_generate_merged_ids_padding_removal(tokenizer, input_ids, target_ids) without return types