code: def _build_fn_orthogonal_i_5(
      input_shape,
  ):  # `orthogonal(gain=Real(0.6, 1.6))`
      model = Sequential(
          [
              Dense(Integer(50, 100), input_shape=input_shape),
              Dense(
                  1, kernel_initializer=orthogonal(gain=Real(0.6, 1.6))
              ),
          ]
      )
      model.compile(
          optimizer="adam",
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
      return model
  
imports: from hyperparameter_hunter import Environment, CVExperiment, Real, Integer, Categorical
  from hyperparameter_hunter import DummyOptPro, BayesianOptPro
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data, get_diabetes_data
  import pandas
  import pytest
  from keras.callbacks import ReduceLROnPlateau
  from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape
  from keras.models import Sequential
  from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
  from keras.initializers import glorot_normal, orthogonal, Orthogonal
  from sklearn.datasets import load_digits
label_desc: Use image node_id 24 for calling a global function with example usage: _build_fn_orthogonal_i_5(input_shape) and returns: model