code: def _set_default_distributions(
      distributions: Dict[str, Union[str, float]]
  ) -> Dict[str, Union[str, float]]:
      default_distributions = {
          "continuous": "gaussian",
          "binary": "logit",
          "categorical": "logit",
          "weight": "uniform",
          "intercept": "uniform",
          "count": 0.05,
      }
  
      if distributions is None:
          return default_distributions
      # overwrite default with input data (if set)
      default_distributions.update(distributions)
      return default_distributions
  
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 10 for calling a global function with example usage: _set_default_distributions(distributions) and returns: default_distributions, default_distributions