code: def init_kf_pykalman(
      state_dim,
      measurement_dim=None,
      state_transition=None,
      process_noise=None,
      measurement_noise=None,
      measurement_function=None,
      initial_state=None,
      initial_state_covariance=None,
      transition_offsets=None,
      measurement_offsets=None,
      estimate_matrices=None,
      denoising=False,
  ):
      """Initiate instance of `pykalman-bardo`'s `KalmanFilter`."""
      from pykalman.standard import KalmanFilter
  
      em_vars = get_params_mapping(params=estimate_matrices)
      kf_pykalman = KalmanFilter(
          transition_matrices=state_transition,
          observation_matrices=measurement_function,
          transition_covariance=process_noise,
          observation_covariance=measurement_noise,
          transition_offsets=transition_offsets,
          observation_offsets=measurement_offsets,
          initial_state_mean=initial_state,
          initial_state_covariance=initial_state_covariance,
          n_dim_state=state_dim,
          n_dim_obs=measurement_dim,
          em_vars=em_vars,
      )
      return kf_pykalman
  
file_path: sktime/sktime/transformations/series/tests/test_kalman_filter.py
imports: import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal
  from sktime.transformations.series.kalman_filter import KalmanFilterTransformerFP, KalmanFilterTransformerPK
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 4 for calling a global function with example usage: init_kf_pykalman(state_dim, measurement_dim, state_transition, process_noise, measurement_noise, measurement_function, initial_state, initial_state_covariance, transition_offsets, measurement_offsets, estimate_matrices, denoising) and returns: kf_pykalman