parameters: (self, *args)
return_statement: return is_variable
docstring: Argument validation for distribution args
          Args:
              value (float, list, numpy.ndarray, Variable)
          Raises
              ValueError: if one argument is Variable, all arguments should be Variable
docstring_summary: Argument validation for distribution args
          Args:
              value (float, list, numpy.ndarray, Variable)
          Raises
              ValueError: if one argument is Variable, all arguments should be Variable
docstring_tokens: Argument validation for distribution args Args : value ( float list numpy . ndarray Variable ) Raises ValueError : if one argument is Variable all arguments should be Variable
function: def _validate_args(self, *args):
          """
          Argument validation for distribution args
          Args:
              value (float, list, numpy.ndarray, Variable)
          Raises
              ValueError: if one argument is Variable, all arguments should be Variable
          """
          is_variable = False
          is_number = False
          for arg in args:
              if isinstance(arg, tensor.Variable):
                  is_variable = True
              else:
                  is_number = True
  
          if is_variable and is_number:
              raise ValueError(
                  'if one argument is Variable, all arguments should be Variable')
  
          return is_variable
function_tokens: def _validate_args ( self , * args ) : is_variable = False is_number = False for arg in args : if isinstance ( arg , tensor . Variable ) : is_variable = True else : is_number = True if is_variable and is_number : raise ValueError ( 'if one argument is Variable, all arguments should be Variable' ) return is_variable