class_docstr: Creates faster Filters than [`FilterPyKalmanFilterFactory`][norfair.filter.FilterPyKalmanFilterFactory].
  
  It allows the user to create Kalman Filter optimized for tracking and set its parameters.
  
  Parameters
  ----------
  R : float, optional
      Multiplier for the sensor measurement noise matrix.
  Q : float, optional
      Multiplier for the process uncertainty.
  pos_variance : float, optional
      Multiplier for the initial covariance matrix estimation, only in the entries that correspond to position (not speed) variables.
  pos_vel_covariance : float, optional
      Multiplier for the initial covariance matrix estimation, only in the entries that correspond to the covariance between position and speed.
  vel_variance : float, optional
      Multiplier for the initial covariance matrix estimation, only in the entries that correspond to velocity (not position) variables.
code: def create_filter(self, initial_detection: np.ndarray):
      num_points = initial_detection.shape[0]
      dim_points = initial_detection.shape[1]
      dim_z = dim_points * num_points  # flattened positions
      dim_x = 2 * dim_z  # We need to accommodate for velocities
  
      custom_filter = OptimizedKalmanFilter(
          dim_x,
          dim_z,
          pos_variance=self.pos_variance,
          pos_vel_covariance=self.pos_vel_covariance,
          vel_variance=self.vel_variance,
          q=self.Q,
          r=self.R,
      )
      custom_filter.x[:dim_z] = np.expand_dims(
          initial_detection.flatten(), 0
      ).T
  
      return custom_filter
  
imports: from abc import ABC, abstractmethod
  import numpy
  from filterpy.kalman import KalmanFilter
label_desc: Use image node_id 2 for calling the OptimizedKalmanFilterFactory obj's underlying member method code with example usage: obj.create_filter(initial_detection) and returns: custom_filter