class_docstr: Applies NetVLAD to the input.
  
  This is a fully-differentiable version of "Vector of Locally Aggregated Descriptors" commonly used in image
  retrieval.
  
  See [NetVLAD: CNN architecture for weakly supervised place recognition](https://arxiv.org/abs/1511.07247), and.
  [Towards Learning a Universal Non-Semantic Representation of Speech](https://arxiv.org/abs/2002.12764)
  
  Args:
      num_clusters: The number of clusters to use.
  Input shape:
      3D tensor with shape: `(batch_size, time, feature_dim)`.
  Output shape:
      2D tensor with shape: `(batch_size, feature_dim * num_clusters)`.
code: def get_config(self):
      config = {"num_clusters": self.num_clusters}
      base_config = super().get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
file_path: addons/tensorflow_addons/layers/netvlad.py
imports: import math
  import tensorflow
  from typeguard import typechecked
label_desc: Use image node_id 5 for calling the NetVLAD obj's underlying member method code with example usage: obj.get_config() and returns: dict