docstr: Compute expected result.
docstr_tok: ["Compute","expected","result","."]
code: def _LRN(
      self,
      input_image,
      lrn_depth_radius=5,
      bias=1.0,
      alpha=1.0,
      beta=0.5,
  ):
      """Compute expected result."""
      output = copy.deepcopy(input_image)
      batch_size = input_image.shape[0]
      rows = input_image.shape[1]
      cols = input_image.shape[2]
      depth = input_image.shape[3]
      for b in range(batch_size):
          for r in range(rows):
              for c in range(cols):
                  for d in range(depth):
                      begin = max(0, d - lrn_depth_radius)
                      end = min(depth, d + lrn_depth_radius + 1)
                      patch = input_image[b, r, c, begin:end]
                      output[b, r, c, d] /= np.power(
                          bias + alpha * np.sum(patch * patch), beta
                      )
      return output
  
imports: import copy
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import nn
  from tensorflow.python.ops import random_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the LRNOpTest obj's underlying member method code with example usage: obj._LRN(input_image, lrn_depth_radius, bias, alpha, beta) and returns: output