class_docstr: Wrapper for a LudwigFeatureDict module that allows for iteration over keys.
  
  The purpose of this class is to avoid exposing input and output features as modules of the LLM. This is because we
  only wish to train the underlying model, and having these additional modules can confuse systems like DeepSpeed.
class_docstr_tok: ["Wrapper","for","a","LudwigFeatureDict","module","that","allows","for","iteration","over","keys",".","The","purpose","of","this","class","is","to","avoid","exposing","input","and","output","features","as","modules","of","the","LLM",".","This","is","because","we","only","wish","to","train","the","underlying","model",",","and","having","these","additional","modules","can","confuse","systems","like","DeepSpeed","."]
code: def __len__(self) -> int:
      return len(self.obj)
  
imports: import contextlib
  import logging
  import os
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from transformers import AutoConfig, GenerationConfig
  from ludwig.accounting.used_tokens import get_used_tokens_for_llm
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, MODEL_LLM, PREDICTIONS, TEXT, USED_TOKENS
  from ludwig.features.base_feature import ModuleWrapper, OutputFeature
  from ludwig.features.feature_utils import LudwigFeatureDict
  from ludwig.features.text_feature import TextOutputFeature
  from ludwig.globals import MODEL_WEIGHTS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.modules.training_hooks import NEFTuneHook
  from ludwig.schema.features.base import BaseOutputFeatureConfig, FeatureCollection
  from ludwig.schema.model_types.llm import LLMModelConfig
  from ludwig.utils.augmentation_utils import AugmentationPipelines
  from ludwig.utils.data_utils import clear_data_cache
  from ludwig.utils.llm_quantization_utils import convert_quantized_linear_to_linear
  from ludwig.utils.llm_utils import add_left_padding, generate_merged_ids, get_context_len, get_realigned_target_and_prediction_tensors_for_inference, initialize_adapter, load_pretrained_from_config, pad_target_tensor_for_fine_tuning, remove_left_padding, to_device
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.output_feature_utils import set_output_feature_tensor
  from ludwig.utils.tokenizers import HFTokenizer
  from ludwig.utils.torch_utils import reg_loss
label_desc: Use image node_id 4 for calling the DictWrapper obj's underlying member method code with example usage: obj.__len__() and returns: len