code: def maybe_infer_to_datetimelike(value, convert_dates=False):
      """
      we might have a array (or single object) that is datetime like,
      and no dtype is passed don't change the value unless we find a
      datetime/timedelta set
  
      this is pretty strict in that a datetime/timedelta is REQUIRED
      in addition to possible nulls/string likes
  
      Parameters
      ----------
      value : np.array / Series / Index / list-like
      convert_dates : boolean, default False
         if True try really hard to convert dates (such as datetime.date), other
         leave inferred dtype 'date' alone
  
      """
  
      # TODO: why not timedelta?
      if isinstance(
          value,
          (
              ABCDatetimeIndex,
              ABCPeriodIndex,
              ABCDatetimeArray,
              ABCPeriodArray,
          ),
      ):
          return value
      elif isinstance(value, ABCSeries):
          if isinstance(value._values, ABCDatetimeIndex):
              return value._values
  
      v = value
  
      if not is_list_like(v):
          v = [v]
      v = np.array(v, copy=False)
  
      # we only care about object dtypes
      if not is_object_dtype(v):
          return value
  
      shape = v.shape
      if not v.ndim == 1:
          v = v.ravel()
  
      if not len(v):
          return value
  
      def try_datetime(v):
          # safe coerce to datetime64
          try:
              # GH19671
              v = tslib.array_to_datetime(
                  v, require_iso8601=True, errors="raise"
              )[0]
          except ValueError:
              # we might have a sequence of the same-datetimes with tz's
              # if so coerce to a DatetimeIndex; if they are not the same,
              # then these stay as object dtype, xref GH19671
              try:
                  from pandas._libs.tslibs import conversion
                  from pandas import DatetimeIndex
  
                  values, tz = conversion.datetime_to_datetime64(v)
                  return (
                      DatetimeIndex(values)
                      .tz_localize("UTC")
                      .tz_convert(tz=tz)
                  )
              except (ValueError, TypeError):
                  pass
  
          except Exception:
              pass
  
          return v.reshape(shape)
  
      def try_timedelta(v):
          # safe coerce to timedelta64
  
          # will try first with a string & object conversion
          from pandas import to_timedelta
  
          try:
              return to_timedelta(v)._ndarray_values.reshape(shape)
          except Exception:
              return v.reshape(shape)
  
      inferred_type = lib.infer_datetimelike_array(ensure_object(v))
  
      if inferred_type == "date" and convert_dates:
          value = try_datetime(v)
      elif inferred_type == "datetime":
          value = try_datetime(v)
      elif inferred_type == "timedelta":
          value = try_timedelta(v)
      elif inferred_type == "nat":
          # if all NaT, return as datetime
          if isna(v).all():
              value = try_datetime(v)
          else:
              # We have at least a NaT and a string
              # try timedelta first to avoid spurious datetime conversions
              # e.g. '00:00:01' is a timedelta but technically is also a datetime
              value = try_timedelta(v)
              if lib.infer_dtype(value, skipna=False) in ["mixed"]:
                  # cannot skip missing values, as NaT implies that the string
                  # is actually a datetime
                  value = try_datetime(v)
  
      return value
  
imports: from datetime import datetime, timedelta
  import numpy
  from pandas._libs import lib, tslib, tslibs
  from pandas._libs.tslibs import NaT, OutOfBoundsDatetime, Period, iNaT
  from pandas.compat import PY3, string_types, text_type, to_str
  from .common import _INT64_DTYPE, _NS_DTYPE, _POSSIBLY_CAST_DTYPES, _TD_DTYPE, ensure_int8, ensure_int16, ensure_int32, ensure_int64, ensure_object, is_bool, is_bool_dtype, is_categorical_dtype, is_complex, is_complex_dtype, is_datetime64_dtype, is_datetime64_ns_dtype, is_datetime64tz_dtype, is_datetime_or_timedelta_dtype, is_datetimelike, is_dtype_equal, is_extension_array_dtype, is_extension_type, is_float, is_float_dtype, is_integer, is_integer_dtype, is_object_dtype, is_scalar, is_string_dtype, is_timedelta64_dtype, is_timedelta64_ns_dtype, is_unsigned_integer_dtype, pandas_dtype
  from .dtypes import DatetimeTZDtype, ExtensionDtype, PandasExtensionDtype, PeriodDtype
  from .generic import ABCDatetimeArray, ABCDatetimeIndex, ABCPeriodArray, ABCPeriodIndex, ABCSeries
  from .inference import is_list_like
  from .missing import isna, notna
label_desc: Use image node_id 19 for calling a global function with example usage: maybe_infer_to_datetimelike(value, convert_dates) and returns: value, value, value, value, v, to_timedelta, value, v, DatetimeIndex