class_docstr: Markov Transition Field.
  
  Parameters
  ----------
  image_size : int or float (default = 1.)
      Shape of the output images. If float, it represents a percentage
      of the size of each time series and must be between 0 and 1. Output
      images are square, thus providing the size of one dimension is enough.
  
  n_bins : int (default = 5)
      Number of bins (also known as the size of the alphabet)
  
  strategy : 'uniform', 'quantile' or 'normal' (default = 'quantile')
      Strategy used to define the widths of the bins:
  
      - 'uniform': All bins in each sample have identical widths
      - 'quantile': All bins in each sample have the same number of points
      - 'normal': Bin edges are quantiles from a standard normal distribution
  
  
  overlapping : bool (default = False)
      If False, reducing the image with the blurring kernel
      will be applied on non-overlapping rectangles. If True,
      it will be applied on possibly overlapping squares.
  
  flatten : bool (default = False)
      If True, images are flattened to be one-dimensional.
  
  References
  ----------
  .. [1] Z. Wang and T. Oates, "Encoding Time Series as Images for Visual
         Inspection and Classification Using Tiled Convolutional Neural
         Networks." AAAI Workshop (2015).
  
  Examples
  --------
  >>> from pyts.datasets import load_gunpoint
  >>> from pyts.image import MarkovTransitionField
  >>> X, _, _, _ = load_gunpoint(return_X_y=True)
  >>> transformer = MarkovTransitionField()
  >>> X_new = transformer.transform(X)
  >>> X_new.shape
  (50, 150, 150)
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docstr: Pass.
  
  Parameters
  ----------
  X
      Ignored
  y
      Ignored
  
  Returns
  -------
  self : object
docstr_tok: ["Pass",".","Parameters","--","--","--","--","--","X","Ignored","y","Ignored","Returns","--","--","--","-","self",":","object"]
code: def fit(self, X=None, y=None):
      """Pass.
  
      Parameters
      ----------
      X
          Ignored
      y
          Ignored
  
      Returns
      -------
      self : object
  
      """
      return self
  
file_path: pyts/pyts/image/mtf.py
imports: import numpy
  from math import ceil
  from numba import njit, prange
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
  from ..preprocessing import KBinsDiscretizer
  from ..utils import segmentation
label_desc: Use image node_id 2 for calling the MarkovTransitionField obj's underlying member method code with example usage: obj.fit(X, y) and returns: self