code: def test_multiple_dependencies(
      reduce_dependencies,
      hidden_shape,
      dependent_hidden_shape,
      dependent_hidden_shape2,
  ):
      # setup at least for a single dependency
      hidden_layer = torch.randn(hidden_shape, dtype=torch.float32)
      other_hidden_layer = torch.randn(
          dependent_hidden_shape, dtype=torch.float32
      )
      other_dependencies = {
          "feature_name": other_hidden_layer,
      }
  
      # setup dummy output feature to be root of dependency list
      num_feature_defn = number_feature()
      num_feature_defn["loss"] = {"type": "mean_squared_error"}
      num_feature_defn["dependencies"] = ["feature_name"]
      if len(dependent_hidden_shape) > 2:
          num_feature_defn["reduce_dependencies"] = reduce_dependencies
  
      # Based on specification calculate expected resulting hidden size for
      # with one dependencies
      if (
          reduce_dependencies == "concat"
          and len(hidden_shape) == 2
          and len(dependent_hidden_shape) == 3
      ):
          expected_hidden_size = (
              HIDDEN_SIZE + OTHER_HIDDEN_SIZE * SEQ_SIZE
          )
      else:
          expected_hidden_size = HIDDEN_SIZE + OTHER_HIDDEN_SIZE
  
      # set up if multiple dependencies specified, setup second dependent feature
      if dependent_hidden_shape2:
          other_hidden_layer2 = torch.randn(
              dependent_hidden_shape2, dtype=torch.float32
          )
          other_dependencies["feature_name2"] = other_hidden_layer2
          num_feature_defn["dependencies"].append("feature_name2")
          if len(dependent_hidden_shape2) > 2:
              num_feature_defn[
                  "reduce_dependencies"
              ] = reduce_dependencies
  
          # Based on specification calculate marginal increase in resulting
          # hidden size with two dependencies
          if (
              reduce_dependencies == "concat"
              and len(hidden_shape) == 2
              and len(dependent_hidden_shape2) == 3
          ):
              expected_hidden_size += (
                  dependent_hidden_shape2[-1] * SEQ_SIZE
              )
          else:
              expected_hidden_size += dependent_hidden_shape2[-1]
  
      # Set up dependency reducers.
      dependency_reducers = torch.nn.ModuleDict()
      for feature_name in other_dependencies.keys():
          dependency_reducers[feature_name] = SequenceReducer(
              reduce_mode=reduce_dependencies
          )
  
      # test dependency concatenation
      num_feature_defn["input_size"] = expected_hidden_size
      results = output_feature_utils.concat_dependencies(
          "num_feature",
          num_feature_defn["dependencies"],
          dependency_reducers,
          hidden_layer,
          other_dependencies,
      )
  
      # confirm size of resulting concat_dependencies() call
      if len(hidden_shape) > 2:
          assert results.shape == (
              BATCH_SIZE,
              SEQ_SIZE,
              expected_hidden_size,
          )
      else:
          assert results.shape == (BATCH_SIZE, expected_hidden_size)
  
file_path: ludwig/tests/integration_tests/test_dependencies.py
imports: import logging
  import pytest
  import torch
  from ludwig.combiners.combiners import ConcatCombiner
  from ludwig.constants import CATEGORY, DECODER, NUMBER, SEQUENCE, TYPE
  from ludwig.models.base import BaseModel
  from ludwig.modules.reduction_modules import SequenceReducer
  from ludwig.schema.model_config import ModelConfig
  from ludwig.utils import output_feature_utils
  from tests.integration_tests.utils import generate_output_features_with_dependencies, number_feature
label_desc: Use image node_id 1 for calling a global function with example usage: test_multiple_dependencies(reduce_dependencies, hidden_shape, dependent_hidden_shape, dependent_hidden_shape2) without return types