code: def env_0():
      return Environment(
          train_dataset=get_diabetes_data(target="target"),
          results_path=assets_dir,
          metrics=["mean_absolute_error"],
          cv_type="KFold",
          cv_params=dict(n_splits=2, shuffle=True, random_state=32),
      )
  
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 2 for calling a global function with example usage: env_0() and returns: Environment