code: def _check_input_dtw(x, y, precomputed_cost, dist, method):
      if dist == "precomputed" and method in ["multiscale", "fast"]:
          raise ValueError(
              "The method '{0}' cannot be used with "
              "a precomputed cost. Provide the raw time series or"
              "use one of the methods: 'classic', 'sakoechiba', "
              "'itakura'".format(method)
          )
      if dist == "precomputed":
          cost = check_array(
              precomputed_cost,
              ensure_min_samples=2,
              ensure_min_features=2,
              ensure_2d=True,
              force_all_finite=False,
              dtype="float64",
          )
          n_timestamps_1, n_timestamps_2 = precomputed_cost.shape
      else:
          cost = None
          x = check_array(x, ensure_2d=False, dtype="float64")
          y = check_array(y, ensure_2d=False, dtype="float64")
          if x.ndim != 1:
              raise ValueError("'x' must be a one-dimensional array.")
          if y.ndim != 1:
              raise ValueError("'y' must be a one-dimensional array.")
          n_timestamps_1 = x.size
          n_timestamps_2 = y.size
  
      return x, y, cost, n_timestamps_1, n_timestamps_2
  
imports: import numpy
  from math import ceil, log2, sqrt
  from numba import njit, prange
  from sklearn.utils import check_array
label_desc: Use image node_id 6 for calling a global function with example usage: _check_input_dtw(x, y, precomputed_cost, dist, method) and returns: x,  y,  cost,  n_timestamps_1,  n_timestamps_2