docstr: Validate FractionalMaxPool's result against expected.
  
  Expected result is computed given input_tensor, and pooling region defined
  by row_seq and col_seq.
  
  Args:
    input_tensor: A tensor or numpy ndarray.
    pooling_ratio: A list or tuple of length 4, first and last element be 1.
    pseudo_random: Use pseudo random method to generate pooling sequence.
    overlapping: Use overlapping when pooling.
  
  Returns:
    None
docstr_tok: ["Validate","FractionalMaxPool","'s","result","against","expected",".","Expected","result","is","computed","given","input_tensor",",","and","pooling","region","defined","by","row_seq","and","col_seq",".","Args",":","input_tensor",":","A","tensor","or","numpy","ndarray",".","pooling_ratio",":","A","list","or","tuple","of","length","4",",","first","and","last","element","be","1.","pseudo_random",":","Use","pseudo","random","method","to","generate","pooling","sequence",".","overlapping",":","Use","overlapping","when","pooling",".","Returns",":","None"]
code: def _ValidateFractionalMaxPoolResult(
      self, input_tensor, pooling_ratio, pseudo_random, overlapping
  ):
      """Validate FractionalMaxPool's result against expected.
  
      Expected result is computed given input_tensor, and pooling region defined
      by row_seq and col_seq.
  
      Args:
        input_tensor: A tensor or numpy ndarray.
        pooling_ratio: A list or tuple of length 4, first and last element be 1.
        pseudo_random: Use pseudo random method to generate pooling sequence.
        overlapping: Use overlapping when pooling.
  
      Returns:
        None
      """
      with self.cached_session():
          p, r, c = nn_ops.fractional_max_pool_v2(
              input_tensor,
              pooling_ratio,
              pseudo_random,
              overlapping,
              seed=self._SEED,
          )
          actual, row_seq, col_seq = self.evaluate([p, r, c])
          expected = self._GetExpectedFractionalMaxPoolResult(
              input_tensor, row_seq, col_seq, overlapping
          )
          self.assertShapeEqual(expected, p)
          self.assertAllClose(expected, actual)
  
imports: import math
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import nn_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
label_desc: Use image node_id 4 for calling the FractionalMaxPoolTest obj's underlying member method code with example usage: obj._ValidateFractionalMaxPoolResult(input_tensor, pooling_ratio, pseudo_random, overlapping) without return types