code: def _markov_transition_field(
      X_binned, X_mtm, n_samples, n_timestamps, n_bins
  ):
      X_mtf = np.zeros((n_samples, n_timestamps, n_timestamps))
      for i in prange(n_samples):
          for j in prange(n_timestamps):
              for k in prange(n_timestamps):
                  X_mtf[i, j, k] = X_mtm[
                      i, X_binned[i, j], X_binned[i, k]
                  ]
      return X_mtf
  
file_path: pyts/pyts/image/mtf.py
imports: import numpy
  from math import ceil
  from numba import njit, prange
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
  from ..preprocessing import KBinsDiscretizer
  from ..utils import segmentation
label_desc: Use image node_id 2 for calling a global function with example usage: _markov_transition_field(X_binned, X_mtm, n_samples, n_timestamps, n_bins) and returns: X_mtf