parameters: (data, dayfirst, yearfirst,
                              utc=False, errors="raise",
                              require_iso8601=False, allow_object=False)
docstring: Convert data to array of timestamps.
  
      Parameters
      ----------
      data : np.ndarray[object]
      dayfirst : bool
      yearfirst : bool
      utc : bool, default False
          Whether to convert timezone-aware timestamps to UTC
      errors : {'raise', 'ignore', 'coerce'}
      allow_object : bool
          Whether to return an object-dtype ndarray instead of raising if the
          data contains more than one timezone.
  
      Returns
      -------
      result : ndarray
          np.int64 dtype if returned values represent UTC timestamps
          np.datetime64[ns] if returned values represent wall times
          object if mixed timezones
      inferred_tz : tzinfo or None
  
      Raises
      ------
      ValueError : if data cannot be converted to datetimes
docstring_summary: Convert data to array of timestamps.
docstring_tokens: Convert data to array of timestamps .
function: def objects_to_datetime64ns(data, dayfirst, yearfirst,
                              utc=False, errors="raise",
                              require_iso8601=False, allow_object=False):
      """
      Convert data to array of timestamps.
  
      Parameters
      ----------
      data : np.ndarray[object]
      dayfirst : bool
      yearfirst : bool
      utc : bool, default False
          Whether to convert timezone-aware timestamps to UTC
      errors : {'raise', 'ignore', 'coerce'}
      allow_object : bool
          Whether to return an object-dtype ndarray instead of raising if the
          data contains more than one timezone.
  
      Returns
      -------
      result : ndarray
          np.int64 dtype if returned values represent UTC timestamps
          np.datetime64[ns] if returned values represent wall times
          object if mixed timezones
      inferred_tz : tzinfo or None
  
      Raises
      ------
      ValueError : if data cannot be converted to datetimes
      """
      assert errors in ["raise", "ignore", "coerce"]
  
      # if str-dtype, convert
      data = np.array(data, copy=False, dtype=np.object_)
  
      try:
          result, tz_parsed = tslib.array_to_datetime(
              data,
              errors=errors,
              utc=utc,
              dayfirst=dayfirst,
              yearfirst=yearfirst,
              require_iso8601=require_iso8601
          )
      except ValueError as e:
          try:
              values, tz_parsed = conversion.datetime_to_datetime64(data)
              # If tzaware, these values represent unix timestamps, so we
              #  return them as i8 to distinguish from wall times
              return values.view('i8'), tz_parsed
          except (ValueError, TypeError):
              raise e
  
      if tz_parsed is not None:
          # We can take a shortcut since the datetime64 numpy array
          #  is in UTC
          # Return i8 values to denote unix timestamps
          return result.view('i8'), tz_parsed
      elif is_datetime64_dtype(result):
          # returning M8[ns] denotes wall-times; since tz is None
          #  the distinction is a thin one
          return result, tz_parsed
      elif is_object_dtype(result):
          # GH#23675 when called via `pd.to_datetime`, returning an object-dtype
          #  array is allowed.  When called via `pd.DatetimeIndex`, we can
          #  only accept datetime64 dtype, so raise TypeError if object-dtype
          #  is returned, as that indicates the values can be recognized as
          #  datetimes but they have conflicting timezones/awareness
          if allow_object:
              return result, tz_parsed
          raise TypeError(result)
      else:  # pragma: no cover
          # GH#23675 this TypeError should never be hit, whereas the TypeError
          #  in the object-dtype branch above is reachable.
          raise TypeError(result)
function_tokens: def objects_to_datetime64ns ( data , dayfirst , yearfirst , utc = False , errors = "raise" , require_iso8601 = False , allow_object = False ) : assert errors in [ "raise" , "ignore" , "coerce" ] # if str-dtype, convert data = np . array ( data , copy = False , dtype = np . object_ ) try : result , tz_parsed = tslib . array_to_datetime ( data , errors = errors , utc = utc , dayfirst = dayfirst , yearfirst = yearfirst , require_iso8601 = require_iso8601 ) except ValueError as e : try : values , tz_parsed = conversion . datetime_to_datetime64 ( data ) # If tzaware, these values represent unix timestamps, so we #  return them as i8 to distinguish from wall times return values . view ( 'i8' ) , tz_parsed except ( ValueError , TypeError ) : raise e if tz_parsed is not None : # We can take a shortcut since the datetime64 numpy array #  is in UTC # Return i8 values to denote unix timestamps return result . view ( 'i8' ) , tz_parsed elif is_datetime64_dtype ( result ) : # returning M8[ns] denotes wall-times; since tz is None #  the distinction is a thin one return result , tz_parsed elif is_object_dtype ( result ) : # GH#23675 when called via `pd.to_datetime`, returning an object-dtype #  array is allowed.  When called via `pd.DatetimeIndex`, we can #  only accept datetime64 dtype, so raise TypeError if object-dtype #  is returned, as that indicates the values can be recognized as #  datetimes but they have conflicting timezones/awareness if allow_object : return result , tz_parsed raise TypeError ( result ) else : # pragma: no cover # GH#23675 this TypeError should never be hit, whereas the TypeError #  in the object-dtype branch above is reachable. raise TypeError ( result )