code: def cyslopes(dataset, t0):
      """Compute slopes.
  
      Parameters
      ----------
      dataset : array-like, shape=(n_ts, sz, d), dtype=float64
      t0 : int32
  
      Returns
      -------
      dataset_out : array-like, shape=(n_ts, d), dtype=float64
      """
      n_ts, sz, d = dataset.shape
      dataset_out = np.empty((n_ts, d))
      vec_t = np.arange(t0, t0 + sz).reshape((-1, 1))
      for i in prange(n_ts):
          for di in range(d):
              with objmode(dataset_out_i_di="float64"):
                  dataset_out_i_di = (
                      LinearRegression()
                      .fit(vec_t, dataset[i, :, di].reshape((-1, 1)))
                      .coef_[0]
                  )
              dataset_out[i, di] = dataset_out_i_di
      return dataset_out
  
file_path: tslearn/tslearn/metrics/cysax.py
imports: import numpy
  from numba import njit, objmode, prange
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 4 for calling a global function with example usage: cyslopes(dataset, t0) and returns: dataset_out