docstr: Computes the training loss for the model.
  
  Args:
      targets: A dictionary of target names to target tensors.
      predictions: A dictionary of output names to output tensors.
      regularization_type: One of 'l1', 'l2', 'l1_l2', or None.
      regularization_lambda: The regularization lambda.
  
  Returns:
      A tuple of the loss tensor and a dictionary of loss for every
      output feature.
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code: def train_loss(
      self,
      targets,
      predictions,
      regularization_type: Optional[str] = None,
      regularization_lambda: Optional[float] = None,
  ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
      """Computes the training loss for the model.
  
      Args:
          targets: A dictionary of target names to target tensors.
          predictions: A dictionary of output names to output tensors.
          regularization_type: One of 'l1', 'l2', 'l1_l2', or None.
          regularization_lambda: The regularization lambda.
  
      Returns:
          A tuple of the loss tensor and a dictionary of loss for every
          output feature.
      """
      train_loss = 0
      of_train_losses = {}
      for of_name, of_obj in self.output_features.items():
          _targets, _predictions = targets, predictions
          if isinstance(of_obj, TextOutputFeature):
              _predictions = {of_name: _predictions}
  
              # Update the target tensor to enable text metric evaluation. This pads the target tensor with -100s
              # to match the prediction length and depends on how much of the target tensor was included in the
              # forward pass.
              _targets = self._update_target_tensor_for_finetuning(
                  _targets, _predictions, of_name
              )
  
          # TODO(Arnav): Seems like doing this again and going between these format types in unnecessary, but
          # refactor so that we don't have to do this at a later point.
          predictions = {}
          for key, _ in _predictions[of_name].items():
              set_output_feature_tensor(
                  predictions, of_name, key, _predictions[of_name][key]
              )
          _predictions = predictions
  
          of_train_loss = of_obj.train_loss(
              _targets[of_name], _predictions, of_name
          )
          train_loss += of_obj.loss.weight * of_train_loss
          of_train_losses[of_name] = of_train_loss
  
      additional_losses = self.losses()
      if additional_losses:
          train_loss += torch.sum(
              torch.stack(additional_losses)
          )  # other losses
  
      # Add regularization loss
      if regularization_type is not None and regularization_lambda != 0:
          train_loss += reg_loss(
              self,
              regularization_type,
              l1=regularization_lambda,
              l2=regularization_lambda,
          )
  
      return train_loss, of_train_losses
  
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 21 for calling the LLM obj's underlying member method code with example usage: obj.train_loss(targets, predictions, regularization_type, regularization_lambda) and returns: train_loss,  of_train_losses