docstr: Update target tensor for fine-tuning.
  
  This method removes left padding from target tensors, adds a pad token to the end of the target tensors,
  and pads the target tensors with -100 to ensure equal length for loss computation. It then realigns the
  target tensors with the prediction tensors.
  
  Args:
      targets (Dict[str, torch.Tensor]): A dictionary containing the target tensors.
      predictions (Dict[str, torch.Tensor]): A dictionary containing the predicted tensors.
      of_name (str): The name of the target tensor.
  
  Returns:
      Dict[str, torch.Tensor]: A dictionary containing the updated target tensors aligned with predictions.
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code: def _update_target_tensor_for_finetuning(
      self,
      targets: Dict[str, torch.Tensor],
      predictions: Dict[str, torch.Tensor],
      of_name: str,
  ) -> Dict[str, torch.Tensor]:
      """Update target tensor for fine-tuning.
  
      This method removes left padding from target tensors, adds a pad token to the end of the target tensors,
      and pads the target tensors with -100 to ensure equal length for loss computation. It then realigns the
      target tensors with the prediction tensors.
  
      Args:
          targets (Dict[str, torch.Tensor]): A dictionary containing the target tensors.
          predictions (Dict[str, torch.Tensor]): A dictionary containing the predicted tensors.
          of_name (str): The name of the target tensor.
  
      Returns:
          Dict[str, torch.Tensor]: A dictionary containing the updated target tensors aligned with predictions.
      """
      # Remove left padding from target tensors since we also do this for the model's forward pass when we
      # concatenate the input_ids with the target_ids. We also need to add the pad token to the end of the
      # target tensors.
      targets_without_padding = []
      lengths = []
  
      pad_token_tensor = torch.tensor([self.tokenizer.pad_token_id])
      for target in targets[of_name]:
          target = remove_left_padding(target, self.tokenizer)[0]
          target = torch.cat(
              [target, pad_token_tensor.to(device=target.device)],
              dim=-1,
          ).unsqueeze(0)
          targets_without_padding.append(target)
          lengths.append(target.shape[1])
  
      # We need all target tensors to have the same length for the loss computation. We pad the target
      # tensors with -100 since we want to negate all tokens that are not target_ids during the softmax
      # cross entropy loss computation. This ensures that the loss is computed only for the target tokens.
      max_length = max(lengths)
      for i, target in enumerate(targets_without_padding):
          targets_without_padding[i] = add_left_padding(
              targets_without_padding[i][0],
              max_length,
              IGNORE_INDEX_TOKEN_ID,
          )
  
      targets[of_name] = torch.stack(targets_without_padding, dim=0).to(
          dtype=targets[of_name].dtype,
          device=targets[of_name].device,
      )
  
      # Re-align target tensors without padding to have equal length before realigning with the prediction
      # tensors. Padding left with -100 to match the length of the target tensor masks the input ids during
      # softmax cross entropy loss computation. This ensures that the loss is computed only for the target
      # token IDs. Examples:
      # BERTLMHead: https://github.com/huggingface/transformers/blob/v4.29.1/src/transformers/models/bert/modeling_bert.py#L1216-L1219 # noqa
      # GPTNeoForCausalLM: https://github.com/huggingface/transformers/blob/v4.29.1/src/transformers/models/gpt_neo/modeling_gpt_neo.py#L736 # noqa
      _targets = pad_target_tensor_for_fine_tuning(
          targets, predictions, self.model_inputs, of_name
      )
  
      return _targets
  
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 29 for calling the LLM obj's underlying member method code with example usage: obj._update_target_tensor_for_finetuning(targets, predictions, of_name) and returns: _targets