code: def get_pima_indians_data(target="class"):
      """Get the Pima Indians Diabetes binary classification dataset, formatted as a DataFrame
  
      Parameters
      ----------
      target: String, default="class"
          What to name the column in `df` that contains the target output values
  
      Returns
      -------
      df: `pandas.DataFrame`
          The Pima Indians dataset, of shape (768, 8 + 1), with column names of:
              "pregnancies",
              "glucose",
              "bp" (shortened from "blood_pressure"),
              "skin_thickness",
              "insulin",
              "bmi",
              "dpf" (shortened from "diabetes_pedigree_function"),
              "age",
              "class" (or given `target` column)
  
      Notes
      -----
      This dataset is originally from the National Institute of Diabetes and Digestive and Kidney
      Diseases. Thanks to Jason Brownlee (of MachineLearningMastery.com), who has generously made a
      public repository to collect copies of all the datasets he uses in his tutorials
  
      Examples
      --------
      >>> get_pima_indians_data().head()
         pregnancies  glucose  bp  skin_thickness  insulin   bmi    dpf  age  class
      0            6      148  72              35        0  33.6  0.627   50      1
      1            1       85  66              29        0  26.6  0.351   31      0
      2            8      183  64               0        0  23.3  0.672   32      1
      3            1       89  66              23       94  28.1  0.167   21      0
      4            0      137  40              35      168  43.1  2.288   33      1
      """
      input_cols = [
          "pregnancies",
          "glucose",
          "bp",
          "skin_thickness",
          "insulin",
          "bmi",
          "dpf",
          "age",
      ]
      df = pd.read_csv(
          "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv",
          names=(input_cols + [target]),
      )
      return df
  
file_path: hyperparameter_hunter/hyperparameter_hunter/utils/learning_utils.py
imports: import pandas
  from sklearn.datasets import load_boston, load_breast_cancer, load_diabetes, load_iris, make_classification
label_desc: Use image node_id 2 for calling a global function with example usage: get_pima_indians_data(target) and returns: df