docstr: Get expected fractional average pooling result.
  
  row_seq and col_seq together defines the fractional pooling region.
  
  Args:
    input_tensor: Original input tensor, assuming it is a 4-D tensor, with
      dimension as [batch, height/row, width/column, channels/depth].
    row_seq: Cumulative pooling sequence along row.
    col_seq: Cumulative pooling sequence along column.
    overlapping: Use overlapping when doing pooling.
  
  Returns:
    A 4-D tensor that is the result of average pooling on input_tensor based
      on pooling region defined by row_seq and col_seq, conditioned on whether
      or not overlapping is used.
code: def _GetExpectedFractionalAvgPoolResult(
      self, input_tensor, row_seq, col_seq, overlapping
  ):
      """Get expected fractional average pooling result.
  
      row_seq and col_seq together defines the fractional pooling region.
  
      Args:
        input_tensor: Original input tensor, assuming it is a 4-D tensor, with
          dimension as [batch, height/row, width/column, channels/depth].
        row_seq: Cumulative pooling sequence along row.
        col_seq: Cumulative pooling sequence along column.
        overlapping: Use overlapping when doing pooling.
  
      Returns:
        A 4-D tensor that is the result of average pooling on input_tensor based
          on pooling region defined by row_seq and col_seq, conditioned on whether
          or not overlapping is used.
      """
      input_shape = input_tensor.shape
      output_shape = (
          input_shape[0],
          len(row_seq) - 1,
          len(col_seq) - 1,
          input_shape[3],
      )
      output_tensor = np.zeros(
          shape=output_shape, dtype=input_tensor.dtype
      )
      for batch in range(input_shape[0]):
          for channel in range(input_shape[3]):
              two_dim_slice = input_tensor[batch, :, :, channel]
              tmp = self._AvgPoolAlongRows(
                  two_dim_slice, row_seq, overlapping
              )
              output_tensor[
                  batch, :, :, channel
              ] = self._AvgPoolAlongCols(tmp, col_seq, overlapping)
  
      return output_tensor
  
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 3 for calling the FractionalAvgTest obj's underlying member method code with example usage: obj._GetExpectedFractionalAvgPoolResult(input_tensor, row_seq, col_seq, overlapping) and returns: output_tensor