code: def forward(
      self, inputs: Dict[str, torch.Tensor], feature_name: str
  ) -> Dict[str, torch.Tensor]:
      logits = output_feature_utils.get_output_feature_tensor(
          inputs, feature_name, self.logits_key
      )
      probabilities = torch.sigmoid(logits)
  
      predictions = torch.greater_equal(probabilities, self.threshold)
      predictions = predictions.type(torch.int64)
  
      return {
          self.predictions_key: predictions,
          self.probabilities_key: probabilities,
          self.logits_key: logits,
      }
  
imports: import logging
  from typing import Any, Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROC_COLUMN, SET
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.features.feature_utils import set_str_to_idx
  from ludwig.schema.features.set_feature import SetInputFeatureConfig, SetOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.strings_utils import create_vocabulary, UNKNOWN_SYMBOL
  from ludwig.utils.tokenizers import get_tokenizer_from_registry, TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the _SetPredict obj's underlying member method code with example usage: obj.forward(inputs, feature_name) and returns: dict