code: def forward(self, inputs):
      assert isinstance(inputs, torch.Tensor)
      assert inputs.dtype in [torch.bool, torch.int64, torch.float32]
      assert len(inputs.shape) == 1 or (
          len(inputs.shape) == 2 and inputs.shape[1] == 1
      )
  
      if len(inputs.shape) == 1:
          inputs = inputs[:, None]
  
      # Inputs to the binary encoder could be of dtype torch.bool. Linear layer
      # weights are of dtype torch.float32. The inputs and the weights need to
      # be of the same dtype.
      if inputs.dtype == torch.bool:
          inputs = inputs.type(torch.float32)
  
      encoder_outputs = self.encoder_obj(inputs)
      return encoder_outputs
  
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 BinaryInputFeature obj's underlying member method code with example usage: obj.forward(inputs) and returns: encoder_outputs