code: def _markov_transition_matrix(
      X_binned, n_samples, n_timestamps, n_bins
  ):
      X_mtm = np.zeros((n_samples, n_bins, n_bins))
      for i in prange(n_samples):
          for j in prange(n_timestamps - 1):
              X_mtm[i, X_binned[i, j], X_binned[i, j + 1]] += 1
      return X_mtm
  
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 1 for calling a global function with example usage: _markov_transition_matrix(X_binned, n_samples, n_timestamps, n_bins) and returns: X_mtm