code: def reshape_logits(
      output_feature: OutputFeature, logits: torch.Tensor
  ) -> torch.Tensor:
      """Add logits for the oposite class if the output feature is category with two classes.
  
      This is needed because LightGBM classifier only returns logits for one class.
      """
      if (
          isinstance(output_feature, CategoryOutputFeature)
          and output_feature.num_classes == 2
      ):
          # add logits for the oposite class (LightGBM classifier only returns logits for one class)
          logits = logits.view(-1, 1)
          logits = torch.cat([-logits, logits], dim=1)
  
      return logits
  
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 8 for calling a global function with example usage: reshape_logits(output_feature, logits) and returns: logits