code: def to_datetime(
      arg,
      errors: str = "raise",
      dayfirst: bool = False,
      yearfirst: bool = False,
      utc: bool = None,
      format: str = None,
      exact: bool = True,
      unit: str = None,
      infer_datetime_format: bool = False,
      origin: Any = "unix",
      cache: bool = True,
  ):
      """
      Convert argument to datetime.
  
      Parameters
      ----------
      arg : int, float, str, datetime, list, tuple, 1-d array, Series DataFrame/dict-like
          The object to convert to a datetime.
      errors : {'ignore', 'raise', 'coerce'}, default 'raise'
          - If 'raise', then invalid parsing will raise an exception.
          - If 'coerce', then invalid parsing will be set as NaT.
          - If 'ignore', then invalid parsing will return the input.
      dayfirst : bool, default False
          Specify a date parse order if `arg` is str or its list-likes.
          If True, parses dates with the day first, eg 10/11/12 is parsed as
          2012-11-10.
          Warning: dayfirst=True is not strict, but will prefer to parse
          with day first (this is a known bug, based on dateutil behavior).
      yearfirst : bool, default False
          Specify a date parse order if `arg` is str or its list-likes.
  
          - If True parses dates with the year first, eg 10/11/12 is parsed as
            2010-11-12.
          - If both dayfirst and yearfirst are True, yearfirst is preceded (same
            as dateutil).
  
          Warning: yearfirst=True is not strict, but will prefer to parse
          with year first (this is a known bug, based on dateutil behavior).
      utc : bool, default None
          Return UTC DatetimeIndex if True (converting any tz-aware
          datetime.datetime objects as well).
      format : str, default None
          The strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
          all the way up to nanoseconds.
          See strftime documentation for more information on choices:
          https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
      exact : bool, True by default
          Behaves as:
          - If True, require an exact format match.
          - If False, allow the format to match anywhere in the target string.
  
      unit : str, default 'ns'
          The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
          integer or float number. This will be based off the origin.
          Example, with unit='ms' and origin='unix' (the default), this
          would calculate the number of milliseconds to the unix epoch start.
      infer_datetime_format : bool, default False
          If True and no `format` is given, attempt to infer the format of the
          datetime strings, and if it can be inferred, switch to a faster
          method of parsing them. In some cases this can increase the parsing
          speed by ~5-10x.
      origin : scalar, default 'unix'
          Define the reference date. The numeric values would be parsed as number
          of units (defined by `unit`) since this reference date.
  
          - If 'unix' (or POSIX) time; origin is set to 1970-01-01.
          - If 'julian', unit must be 'D', and origin is set to beginning of
            Julian Calendar. Julian day number 0 is assigned to the day starting
            at noon on January 1, 4713 BC.
          - If Timestamp convertible, origin is set to Timestamp identified by
            origin.
      cache : bool, default True
          If True, use a cache of unique, converted dates to apply the datetime
          conversion. May produce significant speed-up when parsing duplicate
          date strings, especially ones with timezone offsets. The cache is only
          used when there are at least 50 values. The presence of out-of-bounds
          values will render the cache unusable and may slow down parsing.
  
      Returns
      -------
      datetime
          If parsing succeeded.
          Return type depends on input:
  
          - list-like: DatetimeIndex
          - Series: Series of datetime64 dtype
          - scalar: Timestamp
  
          In case when it is not possible to return designated types (e.g. when
          any element of input is before Timestamp.min or after Timestamp.max)
          return will have datetime.datetime type (or corresponding
          array/Series).
  
      See Also
      --------
      DataFrame.astype : Cast argument to a specified dtype.
      to_timedelta : Convert argument to timedelta.
      convert_dtypes : Convert dtypes.
  
      Examples
      --------
      Assembling a datetime from multiple columns of a DataFrame. The keys can be
      common abbreviations like ['year', 'month', 'day', 'minute', 'second',
      'ms', 'us', 'ns']) or plurals of the same
  
      >>> import mars.dataframe as md
  
      >>> df = md.DataFrame({'year': [2015, 2016],
      ...                    'month': [2, 3],
      ...                    'day': [4, 5]})
      >>> md.to_datetime(df).execute()
      0   2015-02-04
      1   2016-03-05
      dtype: datetime64[ns]
  
      If a date does not meet the `timestamp limitations
      <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
      #timeseries-timestamp-limits>`_, passing errors='ignore'
      will return the original input instead of raising any exception.
  
      Passing errors='coerce' will force an out-of-bounds date to NaT,
      in addition to forcing non-dates (or non-parseable dates) to NaT.
  
      >>> md.to_datetime('13000101', format='%Y%m%d', errors='ignore').execute()
      datetime.datetime(1300, 1, 1, 0, 0)
      >>> md.to_datetime('13000101', format='%Y%m%d', errors='coerce').execute()
      NaT
  
      Passing infer_datetime_format=True can often-times speedup a parsing
      if its not an ISO8601 format exactly, but in a regular format.
  
      >>> s = md.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000)
      >>> s.head().execute()
      0    3/11/2000
      1    3/12/2000
      2    3/13/2000
      3    3/11/2000
      4    3/12/2000
      dtype: object
  
      Using a unix epoch time
  
      >>> md.to_datetime(1490195805, unit='s').execute()
      Timestamp('2017-03-22 15:16:45')
      >>> md.to_datetime(1490195805433502912, unit='ns').execute()
      Timestamp('2017-03-22 15:16:45.433502912')
  
      .. warning:: For float arg, precision rounding might happen. To prevent
          unexpected behavior use a fixed-width exact type.
  
      Using a non-unix epoch origin
  
      >>> md.to_datetime([1, 2, 3], unit='D',
      ...                origin=md.Timestamp('1960-01-01')).execute()
      DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], \
  dtype='datetime64[ns]', freq=None)
      """
      op = DataFrameToDatetime(
          errors=errors,
          dayfirst=dayfirst,
          yearfirst=yearfirst,
          utc=utc,
          format=format,
          exact=exact,
          unit=unit,
          infer_datetime_format=infer_datetime_format,
          origin=origin,
          cache=cache,
      )
      return op(arg)
  
imports: from functools import partial
  from typing import Any
  import numpy
  import pandas
  from pandas.api.types import is_scalar, is_dict_like
  from ...None import opcodes
  from ...core import recursive_tile
  from ...serialization.serializables import KeyField, StringField, BoolField, AnyField
  from ...tensor import tensor
  from ...tensor.core import TENSOR_CHUNK_TYPE
  from ..core import DATAFRAME_TYPE, SERIES_TYPE, INDEX_TYPE, INDEX_CHUNK_TYPE
  from ..initializer import DataFrame, Series, Index
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index, lazy_import
label_desc: Use image node_id 1 for calling a global function with example usage: to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache) and returns: op