class_docstr: Base model for use in LudwigModule.
  
  Implementations of this class should implement the following methods:
  - type()
  - forward()
docstr: Builds a single input feature from the input feature definition.
docstr_tok: ["Builds","a","single","input","feature","from","the","input","feature","definition","."]
code: def build_single_input(
      feature_config: BaseInputFeatureConfig,
      other_input_features: Optional[Dict[str, InputFeature]],
  ) -> InputFeature:
      """Builds a single input feature from the input feature definition."""
      logger.debug(
          f"Input {feature_config.type} feature {feature_config.name}"
      )
  
      encoder_obj = None
      if feature_config.tied is not None:
          tied_input_feature_name = feature_config.tied
          if tied_input_feature_name in other_input_features:
              encoder_obj = other_input_features[
                  tied_input_feature_name
              ].encoder_obj
  
      return create_input_feature(feature_config, encoder_obj)
  
imports: import contextlib
  import logging
  from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torchmetrics
  from ludwig.combiners.combiners import Combiner
  from ludwig.constants import COMBINED, LOSS, NAME
  from ludwig.encoders.base import Encoder
  from ludwig.features.base_feature import create_passthrough_input_feature, InputFeature, ModuleWrapper, OutputFeature
  from ludwig.features.feature_registries import get_input_type_registry, get_output_type_registry
  from ludwig.features.feature_utils import LudwigFeatureDict
  from ludwig.modules.metric_modules import LudwigMetric
  from ludwig.modules.training_hooks import TrainingHook
  from ludwig.schema.features.base import BaseInputFeatureConfig, BaseOutputFeatureConfig, FeatureCollection
  from ludwig.utils.algorithms_utils import topological_sort_feature_dependencies
  from ludwig.utils.metric_utils import get_scalar_from_ludwig_metric
  from ludwig.utils.misc_utils import get_from_registry
  from ludwig.utils.torch_utils import DEVICE, LudwigModule, reg_loss
  from ludwig.utils.types import TorchDevice
label_desc: Use image node_id 7 for calling the BaseModel obj's underlying member method code with example usage: obj.build_single_input(feature_config, other_input_features) and returns: create_input_feature