class_docstr: Abstract class for "continuous time" samplers, which, given a lower bound
  and upper bound, sample "points" (events) in continuous time from a
  specified interval.
class_docstr_tok: ["Abstract","class","for","``","continuous","time","''","samplers",",","which",",","given","a","lower","bound","and","upper","bound",",","sample","``","points","''","(","events",")","in","continuous","time","from","a","specified","interval","."]
docstr: Returns random points in the real interval between :code:`a` and
  :code:`b`.
  
  Parameters
  ----------
  a
      The lower bound (minimum time value that a sampled point can take)
  b
      Upper bound. Must be greater than a.
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code: def __call__(self, interval_length: float) -> np.ndarray:
      """
      Returns random points in the real interval between :code:`a` and
      :code:`b`.
  
      Parameters
      ----------
      a
          The lower bound (minimum time value that a sampled point can take)
      b
          Upper bound. Must be greater than a.
      """
      raise NotImplementedError()
  
imports: from typing import Tuple
  import numpy
  from gluonts.dataset.stat import ScaleHistogram
  from gluonts.pydantic import BaseModel
label_desc: Use image node_id 2 for calling the ContinuousTimePointSampler obj's underlying member method code with example usage: obj.__call__(interval_length) without return types