code: def get_params(trial, model, task):
      if model == "xgboost":
          params = {
              "learning_rate": trial.suggest_float(
                  "learning_rate", 1e-2, 0.25, log=True
              ),
              "reg_lambda": trial.suggest_float(
                  "reg_lambda", 1e-8, 100.0, log=True
              ),
              "reg_alpha": trial.suggest_float(
                  "reg_alpha", 1e-8, 100.0, log=True
              ),
              "subsample": trial.suggest_float("subsample", 0.1, 1.0),
              "colsample_bytree": trial.suggest_float(
                  "colsample_bytree", 0.1, 1.0
              ),
              "max_depth": trial.suggest_int("max_depth", 1, 9),
              "early_stopping_rounds": trial.suggest_int(
                  "early_stopping_rounds", 100, 500
              ),
              "n_estimators": trial.suggest_categorical(
                  "n_estimators", [7000, 15000, 20000]
              ),
              "tree_method": "hist",
              "random_state": 42,
          }
  
          return params
  
      if model == "logistic_regression":
          if task in CLASSIFICATION_TASKS:
              params = {
                  "C": trial.suggest_float("C", 1e-8, 1e3, log=True),
                  "fit_intercept": trial.suggest_categorical(
                      "fit_intercept", [True, False]
                  ),
                  "solver": trial.suggest_categorical(
                      "solver", ["liblinear", "saga"]
                  ),
                  "penalty": trial.suggest_categorical(
                      "penalty", ["l1", "l2"]
                  ),
                  "n_jobs": -1,
              }
              return params
  
          raise ValueError("Task not supported")
  
      if model == "random_forest":
          params = {
              "n_estimators": trial.suggest_int(
                  "n_estimators", 10, 10000
              ),
              "max_depth": trial.suggest_int("max_depth", 2, 15),
              "max_features": trial.suggest_categorical(
                  "max_features", ["auto", "sqrt", "log2", None]
              ),
              "min_samples_split": trial.suggest_int(
                  "min_samples_split", 2, 20
              ),
              "min_samples_leaf": trial.suggest_int(
                  "min_samples_leaf", 1, 20
              ),
              "bootstrap": trial.suggest_categorical(
                  "bootstrap", [True, False]
              ),
              "n_jobs": -1,
          }
          if task in CLASSIFICATION_TASKS:
              params["criterion"] = trial.suggest_categorical(
                  "criterion", ["gini", "entropy"]
              )
              return params
          if task in REGRESSION_TASKS:
              params["criterion"] = trial.suggest_categorical(
                  "criterion",
                  ["squared_error", "absolute_error", "poisson"],
              )
              return params
          raise ValueError("Task not supported")
  
      if model == "extra_trees":
          params = {
              "n_estimators": trial.suggest_int(
                  "n_estimators", 10, 10000
              ),
              "max_depth": trial.suggest_int("max_depth", 2, 15),
              "max_features": trial.suggest_categorical(
                  "max_features", ["auto", "sqrt", "log2", None]
              ),
              "min_samples_split": trial.suggest_int(
                  "min_samples_split", 2, 20
              ),
              "min_samples_leaf": trial.suggest_int(
                  "min_samples_leaf", 1, 20
              ),
              "bootstrap": trial.suggest_categorical(
                  "bootstrap", [True, False]
              ),
              "n_jobs": -1,
          }
          if task in CLASSIFICATION_TASKS:
              params["criterion"] = trial.suggest_categorical(
                  "criterion", ["gini", "entropy"]
              )
              return params
          if task in REGRESSION_TASKS:
              params["criterion"] = trial.suggest_categorical(
                  "criterion", ["squared_error", "absolute_error"]
              )
              return params
          raise ValueError("Task not supported")
  
      if model == "decision_tree":
          params = {
              "max_depth": trial.suggest_int("max_depth", 1, 15),
              "min_samples_split": trial.suggest_int(
                  "min_samples_split", 2, 20
              ),
              "min_samples_leaf": trial.suggest_int(
                  "min_samples_leaf", 1, 20
              ),
              "max_features": trial.suggest_categorical(
                  "max_features", ["auto", "sqrt", "log2", None]
              ),
              "splitter": trial.suggest_categorical(
                  "splitter", ["best", "random"]
              ),
          }
          if task in CLASSIFICATION_TASKS:
              params["criterion"] = trial.suggest_categorical(
                  "criterion", ["gini", "entropy"]
              )
              return params
          if task in REGRESSION_TASKS:
              params["criterion"] = trial.suggest_categorical(
                  "criterion",
                  [
                      "squared_error",
                      "absolute_error",
                      "friedman_mse",
                      "poisson",
                  ],
              )
              return params
          raise ValueError("Task not supported")
  
      if model == "linear_regression":
          if task in REGRESSION_TASKS:
              params = {
                  "fit_intercept": trial.suggest_categorical(
                      "fit_intercept", [True, False]
                  ),
              }
              return params
          raise ValueError("Task not supported")
  
      if model == "svm":
          if task in CLASSIFICATION_TASKS:
              params = {
                  "C": trial.suggest_float("C", 1e-8, 1e3, log=True),
                  "fit_intercept": trial.suggest_categorical(
                      "fit_intercept", [True, False]
                  ),
                  "penalty": "l2",
                  "max_iter": trial.suggest_int(
                      "max_iter", 1000, 10000
                  ),
              }
              return params
  
          if task in REGRESSION_TASKS:
              params = {
                  "C": trial.suggest_float("C", 1e-8, 1e3, log=True),
                  "fit_intercept": trial.suggest_categorical(
                      "fit_intercept", [True, False]
                  ),
                  "loss": trial.suggest_categorical(
                      "loss",
                      [
                          "epsilon_insensitive",
                          "squared_epsilon_insensitive",
                      ],
                  ),
                  "epsilon": trial.suggest_float(
                      "epsilon", 1e-8, 1e-1, log=True
                  ),
                  "max_iter": trial.suggest_int(
                      "max_iter", 1000, 10000
                  ),
              }
              return params
          raise ValueError("Task not supported")
  
      if model == "ridge":
          params = {
              "alpha": trial.suggest_float(
                  "alpha", 1e-8, 1e3, log=True
              ),
              "fit_intercept": trial.suggest_categorical(
                  "fit_intercept", [True, False]
              ),
              "max_iter": trial.suggest_int("max_iter", 1000, 10000),
          }
          if task in CLASSIFICATION_TASKS:
              return params
          if task in REGRESSION_TASKS:
              return params
          raise ValueError("Task not supported")
  
      if model == "lasso":
          if task in REGRESSION_TASKS:
              params = {
                  "alpha": trial.suggest_float(
                      "alpha", 1e-8, 1e3, log=True
                  ),
                  "fit_intercept": trial.suggest_categorical(
                      "fit_intercept", [True, False]
                  ),
                  "max_iter": trial.suggest_int(
                      "max_iter", 1000, 10000
                  ),
              }
              return params
          raise ValueError("Task not supported")
  
      if model == "knn":
          params = {
              "n_neighbors": trial.suggest_int("n_neighbors", 1, 25),
              "weights": trial.suggest_categorical(
                  "weights", ["uniform", "distance"]
              ),
              "algorithm": trial.suggest_categorical(
                  "algorithm", ["ball_tree", "kd_tree", "brute"]
              ),
              "leaf_size": trial.suggest_int("leaf_size", 1, 100),
              "p": trial.suggest_categorical("p", [1, 2]),
              "metric": trial.suggest_categorical(
                  "metric", ["minkowski", "euclidean", "manhattan"]
              ),
          }
          if task in CLASSIFICATION_TASKS or task in REGRESSION_TASKS:
              return params
          raise ValueError("Task not supported")
  
      return ValueError("Invalid model")
  
imports: import copy
  from collections import defaultdict
  from dataclasses import dataclass
  from functools import partial
  from typing import List, Optional
  import numpy
  from sklearn import ensemble, impute, linear_model
  from sklearn import metrics
  from sklearn import naive_bayes, neighbors, pipeline, preprocessing, svm, tree
  from xgboost import XGBClassifier, XGBRegressor
label_desc: Use image node_id 1 for calling a global function with example usage: get_params(trial, model, task) and returns: ValueError, params, params, params, params, params, params, params, params, params, params, params, params, params, params, params