docstr: Manual test by printing out intermediate result of a small random tensor.
  
  Since _GetExpectedFractionalMaxPoolResult is 'automated', it feel safer to
  have a test case that you can see what's happening.
  This test will generate a small, random, int 2D matrix, and feed it to
  FractionalMaxPool and _GetExpectedFractionalMaxPoolResult.
docstr_tok: ["Manual","test","by","printing","out","intermediate","result","of","a","small","random","tensor",".","Since","_GetExpectedFractionalMaxPoolResult","is","'automated","'",",","it","feel","safer","to","have","a","test","case","that","you","can","see","what","'s","happening",".","This","test","will","generate","a","small",",","random",",","int","2D","matrix",",","and","feed","it","to","FractionalMaxPool","and","_GetExpectedFractionalMaxPoolResult","."]
code: def _testVisually(self):
      """Manual test by printing out intermediate result of a small random tensor.
  
      Since _GetExpectedFractionalMaxPoolResult is 'automated', it feel safer to
      have a test case that you can see what's happening.
      This test will generate a small, random, int 2D matrix, and feed it to
      FractionalMaxPool and _GetExpectedFractionalMaxPoolResult.
      """
      num_rows = 6
      num_cols = 6
      tensor_shape = (1, num_rows, num_cols, 1)
      pseudo_random = False
      for overlapping in True, False:
          print("-" * 70)
          print(
              "Testing FractionalMaxPool with overlapping = {}".format(
                  overlapping
              )
          )
          rand_mat = self._PRNG.randint(10, size=tensor_shape)
          pooling_ratio = [1, math.sqrt(2), math.sqrt(2), 1]
          with self.cached_session():
              p, r, c = nn_ops.fractional_max_pool_v2(
                  rand_mat,
                  pooling_ratio,
                  pseudo_random,
                  overlapping,
                  seed=self._SEED,
              )
              tensor_output, row_seq, col_seq = self.evaluate([p, r, c])
              expected_result = (
                  self._GetExpectedFractionalMaxPoolResult(
                      rand_mat, row_seq, col_seq, overlapping
                  )
              )
              print("row sequence:")
              print(row_seq)
              print("column sequence:")
              print(col_seq)
  
              print("Input:")
              # Print input with pooling region marked.
              for i in range(num_rows):
                  row_to_print = []
                  for j in range(num_cols):
                      if j in col_seq:
                          row_to_print.append("|")
                      row_to_print.append(str(rand_mat[0, i, j, 0]))
                  row_to_print.append("|")
                  if i in row_seq:
                      print("-" * 2 * len(row_to_print))
                  print(" ".join(row_to_print))
              print("-" * 2 * len(row_to_print))
  
              print("Output from FractionalMaxPool:")
              print(tensor_output[0, :, :, 0])
              print("Expected result:")
              print(expected_result[0, :, :, 0])
  
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 5 for calling the FractionalMaxPoolTest obj's underlying member method code with example usage: obj._testVisually() without return types