code: def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
      if torch.jit.isinstance(v, List[Tuple[torch.Tensor, int]]):
          raise ValueError(f"Unsupported input: {v}")
  
      if torch.jit.isinstance(v, List[torch.Tensor]):
          v = torch.stack(v)
  
      if torch.jit.isinstance(v, torch.Tensor):
          return v.to(dtype=torch.float32)
  
      v = [s.strip() for s in v]
      if self.should_lower:
          v = [s.lower() for s in v]
      indices = [self.str2bool.get(s, False) for s in v]
      return torch.tensor(indices, dtype=torch.float32)
  
imports: import logging
  from typing import Dict, List, Tuple, Union
  import numpy
  import torch
  from ludwig.constants import BINARY, COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, PROC_COLUMN
  from ludwig.error import InputDataError
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.schema.features.binary_feature import BinaryInputFeatureConfig, BinaryOutputFeatureConfig
  from ludwig.types import FeatureConfigDict, FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import calibration, output_feature_utils, strings_utils
  from ludwig.utils.eval_utils import average_precision_score, ConfusionMatrix, precision_recall_curve, roc_auc_score, roc_curve
  from ludwig.utils.types import DataFrame, TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the _BinaryPreprocessing obj's underlying member method code with example usage: obj.forward(v) and returns: torch, v