class_docstr: Sample forecast object used for temporal point process inference. Differs
  from standard forecast objects as it does not implement fixed length
  samples. Each sample has a variable length, that is kept in a separate
  :code:`valid_length` attribute.
  
  Importantly, PointProcessSampleForecast does not implement some
  methods (such as :code:`quantile` or :code:`plot`) that are available
  in discrete time forecasts.
  
  Parameters
  ----------
  samples
      A multidimensional array of samples, of shape
      (number_of_samples, max_pred_length, target_dim). The target_dim is
      equal to 2, where the first dimension contains the inter-arrival times
      and the second - categorical marks.
  valid_length
      An array of integers denoting the valid lengths of each sample
      in :code:`samples`. That is, :code:`valid_length[0] == 2` implies
      that only the first two entries of :code:`samples[0, ...]` are
      valid "points".
  start_date
      Starting Timestamp of the sample
  freq
      The time unit of interarrival times
  prediction_interval_length
      The length of the prediction interval for which samples were drawn.
  item_id
      Item ID, if available.
  info
      Optional dictionary of additional information.
code: def plot(self, **kwargs):
      raise NotImplementedError(
          "Plotting not implemented for point process samples"
      )
  
file_path: gluonts/src/gluonts/mx/model/tpp/forecast.py
imports: from typing import Dict, Optional, Union, cast
  import mxnet
  import numpy
  import pandas
  from pandas import to_timedelta
  from gluonts.model.forecast import Forecast
label_desc: Use image node_id 7 for calling the PointProcessSampleForecast obj's underlying member method code with example usage: obj.plot() without return types