code: def random_walks(
      n_ts=100, sz=256, d=1, mu=0.0, std=1.0, random_state=None
  ):
      """Random walk time series generator.
  
      Generate n_ts time series of size sz and dimensionality d.
      Generated time series follow the model:
  
      .. math::
  
          ts[t] = ts[t - 1] + a
  
      where :math:`a` is drawn from a normal distribution of mean mu and standard
      deviation std.
  
      Parameters
      ----------
      n_ts : int (default: 100)
          Number of time series.
      sz : int (default: 256)
          Length of time series (number of time instants).
      d : int (default: 1)
          Dimensionality of time series.
      mu : float (default: 0.)
          Mean of the normal distribution from which random walk steps are drawn.
      std : float (default: 1.)
          Standard deviation of the normal distribution from which random walk
          steps are drawn.
      random_state : integer or numpy.RandomState or None (default: None)
          Generator used to draw the time series. If an integer is given, it
          fixes the seed. Defaults to the global
          numpy random number generator.
  
      Returns
      -------
      numpy.ndarray
          A dataset of random walk time series
  
      Examples
      --------
      >>> random_walks(n_ts=100, sz=256, d=5, mu=0., std=1.).shape
      (100, 256, 5)
      """
      rs = check_random_state(random_state)
      ts = numpy.empty((n_ts, sz, d))
      rnd = rs.randn(n_ts, sz, d) * std + mu
      ts[:, 0, :] = rnd[:, 0, :]
      for t in range(1, sz):
          ts[:, t, :] = ts[:, t - 1, :] + rnd[:, t, :]
      return ts
  
imports: import numpy
  from sklearn.utils import check_random_state
label_desc: Use image node_id 1 for calling a global function with example usage: random_walks(n_ts, sz, d, mu, std, random_state) and returns: ts