class_docstr: Determines a rolling count of events over a given window.
  
  Description:
      Given a list of datetimes, return a rolling count starting
      at the row `gap` rows away from the current row and looking backward over the specified
      time window (by `window_length` and `gap`).
  
      Input datetimes should be monotonic.
  
  Args:
      window_length (int, string, optional): Specifies the amount of data included in each window.
          If an integer is provided, it will correspond to a number of rows. For data with a uniform sampling frequency,
          for example of one day, the window_length will correspond to a period of time, in this case,
          7 days for a window_length of 7.
          If a string is provided, it must be one of pandas' offset alias strings ('1D', '1H', etc),
          and it will indicate a length of time that each window should span.
          The list of available offset aliases can be found at
          https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases.
          Defaults to 3.
      gap (int, string, optional): Specifies a gap backwards from each instance before the
          window of usable data begins. If an integer is provided, it will correspond to a number of rows.
          If a string is provided, it must be one of pandas' offset alias strings ('1D', '1H', etc),
          and it will indicate a length of time between a target instance and the beginning of its window.
          Defaults to 1.
      min_periods (int, optional): Minimum number of observations required for performing calculations
          over the window. Can only be as large as window_length when window_length is an integer.
          When window_length is an offset alias string, this limitation does not exist, but care should be taken
          to not choose a min_periods that will always be larger than the number of observations in a window.
          Defaults to 1.
  
  Note:
      Only offset aliases with fixed frequencies can be used when defining gap and h.
      This means that aliases such as `M` or `W` cannot be used, as they can indicate different
      numbers of days. ('M', because different months have different numbers of days;
      'W' because week will indicate a certain day of the week, like W-Wed, so that will
      indicate a different number of days depending on the anchoring date.)
  
  Note:
      When using an offset alias to define `gap`, an offset alias must also be used to define `window_length`.
      This limitation does not exist when using an offset alias to define `window_length`. In fact,
      if the data has a uniform sampling frequency, it is preferable to use a numeric `gap` as it is more
      efficient.
  
  Examples:
      >>> import pandas as pd
      >>> rolling_count = RollingCount(window_length=3)
      >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
      >>> rolling_count(times).tolist()
      [nan, 1.0, 2.0, 3.0, 3.0]
  
      We can also control the gap before the rolling calculation.
  
      >>> import pandas as pd
      >>> rolling_count = RollingCount(window_length=3, gap=0)
      >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
      >>> rolling_count(times).tolist()
      [1.0, 2.0, 3.0, 3.0, 3.0]
  
      We can also control the minimum number of periods required for the rolling calculation.
  
      >>> import pandas as pd
      >>> rolling_count = RollingCount(window_length=3, min_periods=3, gap=0)
      >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
      >>> rolling_count(times).tolist()
      [nan, nan, 3.0, 3.0, 3.0]
  
      We can also set the window_length and gap using offset alias strings.
      >>> import pandas as pd
      >>> rolling_count = RollingCount(window_length='3min', gap='1min')
      >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
      >>> rolling_count(times).tolist()
      [nan, 1.0, 2.0, 3.0, 3.0]
code: def get_function(self):
      def rolling_count(datetime):
          x = pd.Series(1, index=datetime)
          return apply_rolling_agg_to_series(
              x,
              lambda series: series.count(),
              self.window_length,
              self.gap,
              self.min_periods,
              ignore_window_nans=True,
          )
  
      return rolling_count
  
file_path: featuretools/featuretools/primitives/standard/transform/time_series/rolling_count.py
imports: import pandas
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Datetime, Double
  from featuretools.primitives.base.transform_primitive_base import TransformPrimitive
  from featuretools.primitives.standard.transform.time_series.utils import apply_rolling_agg_to_series
label_desc: Use image node_id 2 for calling the RollingCount obj's underlying member method code with example usage: obj.get_function() and returns: rolling_count, apply_rolling_agg_to_series