code: def store_predictions(train_logits_buffer: torch.Tensor) -> Callable:
      """Create a callback that records the predictions of the model on the training data in ``train_logits_buffer``.
  
      Args:
          train_logits_buffer: 2D tensor of shape (n_samples, n_classes) that stores the predictions of the model.
  
      Returns:
          A callback that records the predictions of the model in ``train_logits_buffer``.
      """
  
      def _callback(env: CallbackEnv) -> None:
          # NOTE: have to copy because the buffer is reused in each iteration
          # NOTE: buffer contains raw logits because we use custom objective functions for binary/multiclass.
          preds = env.model._Booster__inner_predict(data_idx=0).copy()
  
          batch_size = preds.size // env.model._Booster__num_class
          preds = preds.reshape(
              batch_size, env.model._Booster__num_class, order="F"
          )
  
          # override the predictions with the new ones
          data_view = train_logits_buffer.view(-1)
          data_view[:] = torch.from_numpy(preds).reshape(-1)
  
      return _callback
  
imports: from dataclasses import dataclass
  from typing import Callable, Dict, Tuple, Union
  import lightgbm
  import numpy
  import torch
  from lightgbm.callback import CallbackEnv
  from numpy import typing
  from ludwig.constants import NUMBER
  from ludwig.features.base_feature import BaseFeatureMixin, OutputFeature
  from ludwig.features.category_feature import CategoryOutputFeature
  from ludwig.models.base import BaseModel
label_desc: Use image node_id 6 for calling a global function with example usage: store_predictions(train_logits_buffer) and returns: _callback