class_docstr: Unpool the outputs of a maximum pooling operation.
  
  This differs from MaxUnpooling2D in that it uses output_size rather than strides and padding
  to calculate the unpooled tensor. This is because MaxPoolingWithArgMax can map several input
  sizes to the same output size, and specifying the output size avoids ambiguity in the
  inversion process.
  
  This function currently does not support outputs of MaxPoolingWithArgMax in following cases:
  - include_batch_in_index equals true.
  - The max pooling operation results in duplicate values in updates and mask.
  
  Args:
    output_size: A tuple/list of 4 integers specifying (batch_size, height, width, channel).
      The targeted output size.
  Call Args:
    updates: A 4D tensor of shape `(batch_size, height, width, channel)`.
      The pooling result from max pooling.
    mask: A 4D tensor of shape `(batch_size, height, width, channel)`.
      The indices of the maximal values.
  Output shape:
    4D tensor with the same shape as output_size.
code: def get_config(self):
      config = super(MaxUnpooling2DV2, self).get_config()
      config["output_size"] = self.output_size
      return config
  
file_path: addons/tensorflow_addons/layers/max_unpooling_2d_v2.py
imports: import tensorflow
  from typeguard import typechecked
  from typing import Iterable
  from tensorflow_addons.utils.keras_utils import normalize_tuple
label_desc: Use image node_id 3 for calling the MaxUnpooling2DV2 obj's underlying member method code with example usage: obj.get_config() and returns: config