parameters: (self, values)
return_statement: return isin(self.asi8, values.asi8)
docstring: Compute boolean array of whether each value is found in the
          passed set of values.
  
          Parameters
          ----------
          values : set or sequence of values
  
          Returns
          -------
          ndarray[bool]
docstring_summary: Compute boolean array of whether each value is found in the
          passed set of values.
docstring_tokens: Compute boolean array of whether each value is found in the passed set of values .
function: def isin(self, values) -> np.ndarray:
          """
          Compute boolean array of whether each value is found in the
          passed set of values.
  
          Parameters
          ----------
          values : set or sequence of values
  
          Returns
          -------
          ndarray[bool]
          """
          if not hasattr(values, "dtype"):
              values = np.asarray(values)
  
          if values.dtype.kind in ["f", "i", "u", "c"]:
              # TODO: de-duplicate with equals, validate_comparison_value
              return np.zeros(self.shape, dtype=bool)
  
          if not isinstance(values, type(self)):
              inferable = [
                  "timedelta",
                  "timedelta64",
                  "datetime",
                  "datetime64",
                  "date",
                  "period",
              ]
              if values.dtype == object:
                  inferred = lib.infer_dtype(values, skipna=False)
                  if inferred not in inferable:
                      if inferred == "string":
                          pass
  
                      elif "mixed" in inferred:
                          return isin(self.astype(object), values)
                      else:
                          return np.zeros(self.shape, dtype=bool)
  
              try:
                  values = type(self)._from_sequence(values)
              except ValueError:
                  return isin(self.astype(object), values)
  
          try:
              self._check_compatible_with(values)
          except (TypeError, ValueError):
              # Includes tzawareness mismatch and IncompatibleFrequencyError
              return np.zeros(self.shape, dtype=bool)
  
          return isin(self.asi8, values.asi8)
function_tokens: def isin ( self , values ) -> np . ndarray : if not hasattr ( values , "dtype" ) : values = np . asarray ( values ) if values . dtype . kind in [ "f" , "i" , "u" , "c" ] : # TODO: de-duplicate with equals, validate_comparison_value return np . zeros ( self . shape , dtype = bool ) if not isinstance ( values , type ( self ) ) : inferable = [ "timedelta" , "timedelta64" , "datetime" , "datetime64" , "date" , "period" , ] if values . dtype == object : inferred = lib . infer_dtype ( values , skipna = False ) if inferred not in inferable : if inferred == "string" : pass elif "mixed" in inferred : return isin ( self . astype ( object ) , values ) else : return np . zeros ( self . shape , dtype = bool ) try : values = type ( self ) . _from_sequence ( values ) except ValueError : return isin ( self . astype ( object ) , values ) try : self . _check_compatible_with ( values ) except ( TypeError , ValueError ) : # Includes tzawareness mismatch and IncompatibleFrequencyError return np . zeros ( self . shape , dtype = bool ) return isin ( self . asi8 , values . asi8 )