code: def random_walk_blobs(
      n_ts_per_blob=100,
      sz=256,
      d=1,
      n_blobs=2,
      noise_level=1.0,
      random_state=None,
  ):
      """Blob-based random walk time series generator.
  
      Generate n_ts_per_blobs * n_blobs 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.
  
      Each blob contains time series derived from a same seed time series with
      added white noise.
  
      Parameters
      ----------
      n_ts_per_blob : int (default: 100)
          Number of time series in each blob
      sz : int (default: 256)
          Length of time series (number of time instants)
      d : int (default: 1)
          Dimensionality of time series
      n_blobs : int (default: 2)
          Number of blobs
      noise_level : float (default: 1.)
          Standard deviation of white noise added to time series in each blob
      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
      numpy.ndarray
          Labels associated to random walk time series (blob id)
  
      Examples
      --------
      >>> X, y = random_walk_blobs(n_ts_per_blob=100, sz=256, d=5, n_blobs=3)
      >>> X.shape
      (300, 256, 5)
      >>> y.shape
      (300,)
      """
      rs = check_random_state(random_state)
      base_ts = random_walks(
          n_ts=n_blobs, sz=sz, d=d, std=1.0, random_state=rs
      )
      rnd = rs.randn(n_ts_per_blob * n_blobs, sz, d) * noise_level
      ts = numpy.repeat(base_ts, repeats=n_ts_per_blob, axis=0)
      y = numpy.repeat(range(n_blobs), repeats=n_ts_per_blob)
      return ts + rnd, y
  
imports: import numpy
  from sklearn.utils import check_random_state
label_desc: Use image node_id 2 for calling a global function with example usage: random_walk_blobs(n_ts_per_blob, sz, d, n_blobs, noise_level, random_state) and returns: unknown,  y