code: def get_input_size_with_dependencies(
      combiner_output_size: int,
      dependencies: List[str],
      other_output_features,  # Dict[str, "OutputFeature"]
  ):
      """Returns the input size for the first layer of this output feature's FC stack, accounting for dependencies on
      other output features.
  
      In the forward pass, the hidden states of any dependent output features get concatenated with the combiner's output.
      If this output feature depends on other output features, then the input size for this feature's FCStack is the sum
      of the output sizes of other output features + the combiner's output size.
      """
      input_size_with_dependencies = combiner_output_size
      for feature_name in dependencies:
          if other_output_features[feature_name].fc_stack.num_layers:
              input_size_with_dependencies += other_output_features[
                  feature_name
              ].fc_stack.output_shape[-1]
          else:
              # 0-layer FCStack. Use the output feature's input size.
              input_size_with_dependencies += other_output_features[
                  feature_name
              ].input_size
      return input_size_with_dependencies
  
imports: import re
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from ludwig.constants import NAME, PREPROCESSING, SEQUENCE, TEXT, TIMESERIES, TYPE
  from ludwig.utils.data_utils import hash_dict
  from ludwig.utils.strings_utils import get_tokenizer_from_registry, UNKNOWN_SYMBOL
label_desc: Use image node_id 7 for calling a global function with example usage: get_input_size_with_dependencies(combiner_output_size, dependencies, other_output_features) and returns: input_size_with_dependencies