class_docstr: Gramian Angular 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.
  
  sample_range : None or tuple (min, max) (default = (-1, 1))
      Desired range of transformed data. If None, no scaling is performed
      and all the values of the input data must be between -1 and 1.
      If tuple, each sample is scaled between min and max; min must be
      greater than or equal to -1 and max must be lower than or equal to 1.
  
  method : 'summation' or 'difference' (default = 'summation')
      Type of Gramian Angular Field. 's' can be used for 'summation'
      and 'd' for 'difference'.
  
  overlapping : bool (default = False)
      If True, reduce the size of each time series using PAA with possible
      overlapping windows.
  
  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 GramianAngularField
  >>> X, _, _, _ = load_gunpoint(return_X_y=True)
  >>> transformer = GramianAngularField()
  >>> 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/gaf.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 ..approximation import PiecewiseAggregateApproximation
  from ..base import UnivariateTransformerMixin
  from ..preprocessing import MinMaxScaler
label_desc: Use image node_id 2 for calling the GramianAngularField obj's underlying member method code with example usage: obj.fit(X, y) and returns: self