class_docstr: Class for getting data.
class_docstr_tok: ["Class","for","getting","data","."]
docstr: Generate X and y data with X.
  Parameters
  ----------
  shape : tuple
      shape of data to be generated
  m : int
      length of step function for y
  Returns
  -------
  train : ndarray
      generated as a sequence of  reshaped to (LEN, WIDTH)
  labels : ndarray
      generated as a step-function with a step every ``m``. As such,
      each prediction fold during cross-validation have
      a unique level value.
code: def get_data(self, shape, m):
      """Generate X and y data with X.
      Parameters
      ----------
      shape : tuple
          shape of data to be generated
      m : int
          length of step function for y
      Returns
      -------
      train : ndarray
          generated as a sequence of  reshaped to (LEN, WIDTH)
      labels : ndarray
          generated as a step-function with a step every ``m``. As such,
          each prediction fold during cross-validation have
          a unique level value.
      """
      s = shape[0]
      w = shape[1]
  
      train = np.array(range(int(s * w)), dtype="float").reshape((s, w))
      train += 1
  
      labels = np.zeros(train.shape[0])
  
      if not self.proba:
          increment = 10
          for i in range(0, s, m):
              labels[i : i + m] += increment
  
              increment += 10
  
      else:
          labels = np.arange(train.shape[0]) % 2
  
      return train, labels
  
imports: from __future__ import division, print_function, with_statement
  import os
  import shutil
  import warnings
  from abc import abstractmethod
  import numpy
  from ..utils.utils import pickle_save, pickle_load
  from ..utils.dummy import OLS, LogisticRegression, Scale
  from ..externals.sklearn.base import clone
  from ..index import INDEXERS
  from ..ensemble.base import Sequential
  from ..estimators import LayerEnsemble
  from ..parallel import ParallelProcessing, Learner, Transformer, Layer, make_group, Pipeline
label_desc: Use image node_id 2 for calling the Data obj's underlying member method code with example usage: obj.get_data(shape, m) and returns: train,  labels