class_docstr: Reshapes the input as a 2D tensor where each row is an pooling
  example.
  
  Parameters
  ----------
  mode : {'valid', 'ignore_borders', 'wrap_centered'}
      - 'valid' :
          Requires an input that is a multiple of the pooling factor
          (in each direction).
      - 'half' :
          Equivalent to 'valid' if we pre-pad with zeros the input on
          each side by (neib_shape[0]//2, neib_shape[1]//2)
      - 'full' :
          Equivalent to 'valid' if we pre-pad with zeros the input on
          each side by (neib_shape[0] - 1, neib_shape[1] - 1)
      - 'ignore_borders' :
          Same as valid, but will ignore the borders if the shape(s)
          of the input is not a multiple of the pooling factor(s).
      - 'wrap_centered' :
          ?? TODO comment
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code: def get_params(self, node):
      return self.mode
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import theano
  from theano import Op, Apply
  from theano.gof import EnumList
  import theano.tensor
  from theano.gradient import grad_not_implemented
  from theano.gradient import grad_undefined
label_desc: Use image node_id 1 for calling the Images2Neibs obj's underlying member method code with example usage: obj.get_params(node) and returns: self