code: def is_2d_list_like(x):
      """Determine whether a point is 2-dimensional list-like
  
      Parameters
      ----------
      x: List
          Some point to check for 2D list-likeness
  
      Returns
      -------
      Boolean
          True if `x` is 2D list-like. Else False"""
      return np.all([is_list_like(_) for _ in x])
  
imports: from hyperparameter_hunter.settings import G
  from hyperparameter_hunter.space.space_core import Space, normalize_dimensions
  from joblib import Parallel, delayed
  from math import log
  from numbers import Number
  import numpy
  import sys
  import warnings
  from scipy.optimize import fmin_l_bfgs_b
  from sklearn.base import clone, is_regressor
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.multioutput import MultiOutputRegressor
  from sklearn.utils import check_random_state
  from skopt.acquisition import _gaussian_acquisition, gaussian_acquisition_1D
  from skopt.learning import ExtraTreesRegressor, GaussianProcessRegressor, GradientBoostingQuantileRegressor, RandomForestRegressor
  from skopt.learning.gaussian_process.kernels import ConstantKernel, HammingKernel, Matern
  from skopt.utils import create_result, has_gradients
label_desc: Use image node_id 2 for calling a global function with example usage: is_2d_list_like(x) and returns: np