code: def initialize_dummy_model(
      model_initializer, build_fn, wrapper_params
  ):
      """Creates a dummy model with placeholder values wherever hyperparameter options are provided
      via `hyperparameter_hunter.space` classes in order to produce a valid Keras model, albeit one
      with semi-useless values, which also contains attributes injected by
      :mod:`hyperparameter_hunter.importer`, and :mod:`hyperparameter_hunter.tracers` in order to
      keep a record of given hyperparameter choices
  
      Parameters
      ----------
      model_initializer: :class:`keras.wrappers.scikit_learn.<KerasClassifier; KerasRegressor>`
          A descendant of :class:`keras.wrappers.scikit_learn.BaseWrapper` used to build a Keras model
      build_fn: Callable
          The `build_fn` value provided to :meth:`keras.wrappers.scikit_learn.BaseWrapper.__init__`
      wrapper_params: Dict
          Additional parameters given to :meth:`keras.wrappers.scikit_learn.BaseWrapper.__init__`, as
          `sk_params`. Some acceptable values include arguments of `build_fn`; and arguments for the
          `fit`, `predict`, `predict_proba`, and `score` methods. For further information on
          acceptable values see the Keras documentation
  
      Returns
      -------
      dummy: Instance of :class:`keras.wrappers.scikit_learn.<KerasClassifier; KerasRegressor>`
          An initialized, compiled descendant of :class:`keras.wrappers.scikit_learn.BaseWrapper`
      """
      setattr(
          G, "use_dummy_tracer", True
      )  # Handles dummifying params via `ArgumentTracer`
  
      wrapper_params = deepcopy(wrapper_params)
  
      if "input_dim" in wrapper_params:
          wrapper_params["input_shape"] = (wrapper_params["input_dim"],)
          del wrapper_params["input_dim"]
      if ("input_shape" not in wrapper_params) or (
          wrapper_params["input_shape"][0] <= 0
      ):
          wrapper_params["input_shape"] = (1,)
  
      dummy = model_initializer(build_fn=build_fn, **wrapper_params)
  
      # NOTE: Below if/else might be unnecessary since `build_fn` should always be a function
      if dummy.build_fn is None:
          dummy.model = dummy.__call__(
              **dummy.filter_sk_params(dummy.__call__)
          )
      elif not isinstance(dummy.build_fn, (FunctionType, MethodType)):
          dummy.model = dummy.build_fn(
              **dummy.filter_sk_params(dummy.build_fn.__call__)
          )
      else:
          dummy.model = dummy.build_fn(
              **dummy.filter_sk_params(dummy.build_fn)
          )
  
      setattr(G, "use_dummy_tracer", False)
      return dummy
  
imports: from hyperparameter_hunter.settings import G, TEMP_MODULES_DOT_PATH, TEMP_MODULES_DIR_PATH
  from hyperparameter_hunter.compat.keras_helper import parameterize_compiled_keras_model
  from hyperparameter_hunter.space.dimensions import Real, Integer, Categorical
  from hyperparameter_hunter.utils.boltons_utils import remap, default_enter
  from hyperparameter_hunter.utils.general_utils import deep_restricted_update, subdict
  from hyperparameter_hunter.utils.parsing_utils import stringify_model_builder, write_python, build_temp_model_file
  from collections import OrderedDict
  from copy import deepcopy
  from datetime import datetime
  from importlib.util import spec_from_file_location, module_from_spec
  import os
  import re
  import sys
  from types import FunctionType, MethodType
label_desc: Use image node_id 6 for calling a global function with example usage: initialize_dummy_model(model_initializer, build_fn, wrapper_params) and returns: dummy