code: def _unconditional_sample(x, kernel):
      cov_mat = kernel(x)
      y = np.random.multivariate_normal(
          mean=np.zeros(shape=x.shape[0]), cov=cov_mat
      )
      return y.squeeze(), cov_mat
  
imports: from typing import Dict, Hashable, List, Optional, Tuple, Union
  import networkx
  import numpy
  import pandas
  from sklearn.gaussian_process.kernels import RBF, Kernel
  from causalnex.structure import StructureModel
  from causalnex.structure.categorical_variable_mapper import VariableFeatureMapper, validate_schema
label_desc: Use image node_id 15 for calling a global function with example usage: _unconditional_sample(x, kernel) and returns: y,  cov_mat