code: def make_mixed_dataset(
      task,
      n_samples,
      n_features=7,
      n_categories=2,
      n_informative=5,
      random_state=42,
      n_targets=None,
      **kwargs,
  ):
      """Creates a synthetic dataset with mixed data types.
  
      Args:
          task (str): Either "classification" or "regression"
          n_samples (int): Number of samples to generate
          n_features (int): Number of total features to generate
          n_categories (int): Number of features to be categorical
          n_informative (int): Number of informative features
          random_state (int): Random seed for reproducibility
          n_targets (int): Number of targets to generate. n_targets>1 will generate a multi-target dataset
              for regression and multi-class dataset for classification.
              Defaults to 2 classes for classification and 1 for regression
          kwargs: Additional arguments to pass to the make_classification or make_regression function
      """
      assert (
          n_features >= n_categories
      ), "n_features must be greater than or equal to n_categories"
      assert (
          n_informative <= n_features
      ), "n_informative must be less than or equal to n_features"
      assert task in [
          "classification",
          "regression",
      ], "task must be either classification or regression"
      if n_targets is None:
          n_targets = 1 if task == "regression" else 2
      if task == "classification":
          X, y = make_classification(
              n_samples=n_samples,
              n_features=n_features,
              random_state=random_state,
              n_informative=n_informative,
              n_classes=n_targets,
              **kwargs,
          )
      elif task == "regression":
          X, y = make_regression(
              n_samples=n_samples,
              n_features=n_features,
              random_state=random_state,
              n_informative=n_informative,
              n_targets=n_targets,
              **kwargs,
          )
      cat_cols = random.choices(
          list(range(X.shape[-1])), k=n_categories
      )
      num_cols = [i for i in range(X.shape[-1]) if i not in cat_cols]
      for col in cat_cols:
          X[:, col] = pd.qcut(X[:, col], q=4).codes.astype(int)
      col_names = []
      num_col_names = []
      cat_col_names = []
      for i in range(X.shape[-1]):
          if i in cat_cols:
              col_names.append(f"cat_col_{i}")
              cat_col_names.append(f"cat_col_{i}")
          if i in num_cols:
              col_names.append(f"num_col_{i}")
              num_col_names.append(f"num_col_{i}")
      X = pd.DataFrame(X, columns=col_names)
      if n_targets == 1 or task == "classification":
          y = pd.Series(y, name="target")
      else:
          y = pd.DataFrame(
              y, columns=[f"target_{i}" for i in range(n_targets)]
          )
      if task == "classification":
          y = "class_" + y.astype(str)
      data = X.join(y)
      return data, cat_col_names, num_col_names
  
file_path: pytorch_tabular/src/pytorch_tabular/utils/data_utils.py
imports: import gzip
  import os
  import random
  import shutil
  import numpy
  import pandas
  import requests
  import torch
  from pandas import DataFrame, Series
  from sklearn.cluster import KMeans
  from sklearn.datasets import make_classification, make_regression
  from sklearn.preprocessing import LabelEncoder
  from .logger import get_logger
label_desc: Use image node_id 5 for calling a global function with example usage: make_mixed_dataset(task, n_samples, n_features, n_categories, n_informative, random_state, n_targets) and returns: data,  cat_col_names,  num_col_names