code: def generate_patients_datasets(train_path, test_path):
      samples = 237
  
      for samples, path in zip([237, 154], [train_path, test_path]):
          data = DataFrame()
          data["age"] = np.random.randint(20, 71, size=samples)
          data["gender"] = np.where(
              np.random.binomial(1, 0.7, samples) == 1, "male", "female"
          )
          data["diet"] = np.where(
              np.random.binomial(1, 0.1, samples) == 1, "yes", "no"
          )
          data["glucose"] = np.random.uniform(4, 12, size=samples)
          data["platelets"] = np.random.randint(100, 500, size=samples)
          data["cholesterol"] = np.random.uniform(
              4.5, 6.5, size=samples
          )
          data["survival_in_days"] = np.random.randint(
              30, 500, size=samples
          )
          data["outcome"] = np.where(
              np.random.binomial(1, 0.8, size=samples) == 1,
              "dead",
              "alive",
          )
          data["target"] = np.where(
              data["outcome"] == "dead",
              data["survival_in_days"],
              -data["survival_in_days"],
          )
          data = data.drop(["outcome", "survival_in_days"], axis=1)
          data.to_csv(path, header=False, index=False, sep="\t")
  
imports: import collections
  import csv
  import json
  import os
  import random
  import shutil
  import sys
  from pandas import read_csv, DataFrame
  from copy import deepcopy
  import numpy
  from catboost.utils import read_cd
label_desc: Use image node_id 4 for calling a global function with example usage: generate_patients_datasets(train_path, test_path) without return types