code: def _validate_frequency(cls, index, freq, **kwargs):
          
          if is_period_dtype(cls):
              # Frequency validation is not meaningful for Period Array/Index
              return None
  
          inferred = index.inferred_freq
          if index.size == 0 or inferred == freq.freqstr:
              return None
  
          try:
              on_freq = cls._generate_range(start=index[0], end=None,
                                            periods=len(index), freq=freq,
                                            **kwargs)
              if not np.array_equal(index.asi8, on_freq.asi8):
                  raise ValueError
          except ValueError as e:
              if "non-fixed" in str(e):
                  # non-fixed frequencies are not meaningful for timedelta64;
                  #  we retain that error message
                  raise e
              # GH#11587 the main way this is reached is if the `np.array_equal`
              #  check above is False.  This can also be reached if index[0]
              #  is `NaT`, in which case the call to `cls._generate_range` will
              #  raise a ValueError, which we re-raise with a more targeted
              #  message.
              raise ValueError('Inferred frequency {infer} from passed values '
                               'does not conform to passed frequency {passed}'
                               .format(infer=inferred, passed=freq.freqstr))
docs: Validate that a frequency is compatible with the values of a given
          Datetime Array/Index or Timedelta Array/Index
  
          Parameters
          ----------
          index : DatetimeIndex or TimedeltaIndex
              The index on which to determine if the given frequency is valid
          freq : DateOffset
              The frequency to validate