code: def _build_model():
      """Build the model to fit."""
      return Pipeline(
          steps=[
              (
                  "preprocessor",
                  ColumnTransformer(
                      transformers=[
                          (
                              "num",
                              Pipeline(
                                  steps=[
                                      (
                                          "imputer",
                                          SimpleImputer(
                                              strategy="median"
                                          ),
                                      ),
                                      ("scaler", StandardScaler()),
                                  ]
                              ),
                              _NUM_FEATURES,
                          ),
                          ("cat", OneHotEncoder(), _CAT_FEATURES),
                      ]
                  ),
              ),
              (
                  "classifier",
                  RandomForestRegressor(
                      random_state=0, max_depth=7, n_estimators=30
                  ),
              ),
          ]
      )
  
file_path: deepchecks/deepchecks/tabular/datasets/regression/avocado.py
imports: import typing
  from urllib.request import urlopen
  import joblib
  import pandas
  import sklearn
  from category_encoders import OneHotEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from deepchecks.tabular.dataset import Dataset
label_desc: Use image node_id 3 for calling a global function with example usage: _build_model() and returns: Pipeline