class_docstr: Extracts patches from a collection of images
  
  Read more in the :ref:`User Guide <image_feature_extraction>`.
  
  Parameters
  ----------
  patch_size : tuple of ints (patch_height, patch_width)
      the dimensions of one patch
  
  max_patches : integer or float, optional default is None
      The maximum number of patches per image to extract. If max_patches is a
      float in (0, 1), it is taken to mean a proportion of the total number
      of patches.
  
  random_state : int or RandomState
      Pseudo number generator state used for random sampling.
docstr: Do nothing and return the estimator unchanged
  
  This method is just there to implement the usual API and hence
  work in pipelines.
docstr_tok: ["Do","nothing","and","return","the","estimator","unchanged","This","method","is","just","there","to","implement","the","usual","API","and","hence","work","in","pipelines","."]
code: def fit(self, X, y=None):
      """Do nothing and return the estimator unchanged
  
      This method is just there to implement the usual API and hence
      work in pipelines.
      """
      return self
  
imports: from itertools import product
  import numbers
  import numpy
  from scipy import sparse
  from numpy.lib.stride_tricks import as_strided
  from ..utils import check_array, check_random_state
  from ..utils.fixes import astype
  from ..base import BaseEstimator
label_desc: Use image node_id 2 for calling the PatchExtractor obj's underlying member method code with example usage: obj.fit(X, y) and returns: self