human
I wrote the below code

    if isinstance(value, (ABCDatetimeIndex, ABCPeriodIndex)):
        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)
    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):
        try:
            v = tslib.array_to_datetime(v, errors='raise')
        except ValueError:
            try:
                from pandas import to_datetime
                return to_datetime(v)
            except:
                pass
        except:
            pass
        return v.reshape(shape)
    def try_timedelta(v):
        from pandas import to_timedelta
        try:
            return to_timedelta(v)._values.reshape(shape)
        except:
            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 isna(v).all():
            value = try_datetime(v)
        else:
            value = try_timedelta(v)
            if (lib.infer_dtype(value) in ['mixed']):
                value = try_datetime(v)
    return value

What's a good function header?

Here's a good function header?
assistant
#Here's a good function header.

def maybe_infer_to_datetimelike(value, convert_dates=False):