code: def _handle_distribution_sampling(
      distribution: str,
      distribution_func,
      noise_std: float,
      size: Tuple[int],
      root_node: bool,
  ):
      # force scale to be 1 for the root node
      if root_node:
          noise_std = 1
  
      # special sampling syntax
      if distribution == "student-t":
          return distribution_func(df=5, size=size) * noise_std
  
      # default sampling syntax
      return distribution_func(scale=noise_std, size=size)
  
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 3 for calling a global function with example usage: _handle_distribution_sampling(distribution, distribution_func, noise_std, size, root_node) and returns: distribution_func, unknown