code: def from_stumpy_dataset(X):
      """Transform a stumpy-compatible dataset into a tslearn dataset.
  
      Parameters
      ----------
      X: list of arrays of shapes (d, sz_i) if d > 1 or (sz_i, ) otherwise
          stumpy-formatted dataset.
  
      Returns
      -------
      array, shape=(n_ts, sz, d), where sz is the maximum of all array lengths
          tslearn-formatted dataset
  
      Examples
      --------
      >>> stumpy_arr = [numpy.random.randn(10), numpy.random.randn(10)]
      >>> tslearn_arr = from_stumpy_dataset(stumpy_arr)
      >>> tslearn_arr.shape
      (2, 10, 1)
      >>> stumpy_arr = [numpy.random.randn(3, 10), numpy.random.randn(3, 5)]
      >>> tslearn_arr = from_stumpy_dataset(stumpy_arr)
      >>> tslearn_arr.shape
      (2, 10, 3)
      """
  
      def transpose_or_expand(ts):
          if ts.ndim == 1:
              return ts.reshape((-1, 1))
          else:
              return ts.transpose()
  
      return to_time_series_dataset(
          [transpose_or_expand(Xi) for Xi in X]
      )
  
file_path: tslearn/tslearn/utils/cast.py
imports: import warnings
  import numpy
  from sklearn.utils import check_array
  from .utils import check_dataset, ts_size, to_time_series_dataset
label_desc: Use image node_id 7 for calling a global function with example usage: from_stumpy_dataset(X) and returns: to_time_series_dataset, ts, ts