code: def test_max_position_embeddings(self):
      # Test when 'max_position_embeddings' is present in the model configuration
      config = AutoConfig.from_pretrained("huggyllama/llama-7b")
      del config.max_sequence_length
      assert get_context_len(config) == config.max_position_embeddings
  
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 2 for calling the TestSetContextLen obj's underlying member method code with example usage: obj.test_max_position_embeddings() without return types