class_docstr: A layer to unpack a dictionary of sample features and neighbor features.
  
  Missing neighbor inputs will also be created for the functional API.
docstr: Extracts neighbor features and weights from a dictionary of inputs.
  
  This function is a wrapper around `utils.unpack_neighbor_features`. See
  `utils.unpack_neighbor_features` for documentation on the expected input
  format and return values.
  
  Args:
    inputs: Dictionary of `tf.Tensor` features with keys for neighbors and
      weights described by `neighbor_config`.
    keep_rank: Boolean indicating whether each value of `neighbor_features`
      retains the rank from the corresponding value in `sample_features`
      by merging the neighborhood size with the batch_size dimension, or
      contains an extra neighborhood dimension at axis 1. Defaults to `True`.
  
  Returns:
    A tuple (sample_features, neighbor_features, neighbor_weights) of tensors.
    See `utils.unpack_neighbor_features` for a detailed description.
docstr_tok: ["Extracts","neighbor","features","and","weights","from","a","dictionary","of","inputs",".","This","function","is","a","wrapper","around","`","utils.unpack_neighbor_features","`",".","See","`","utils.unpack_neighbor_features","`","for","documentation","on","the","expected","input","format","and","return","values",".","Args",":","inputs",":","Dictionary","of","`","tf.Tensor","`","features","with","keys","for","neighbors","and","weights","described","by","`","neighbor_config","`",".","keep_rank",":","Boolean","indicating","whether","each","value","of","`","neighbor_features","`","retains","the","rank","from","the","corresponding","value","in","`","sample_features","`","by","merging","the","neighborhood","size","with","the","batch_size","dimension",",","or","contains","an","extra","neighborhood","dimension","at","axis","1",".","Defaults","to","`","True","`",".","Returns",":","A","tuple","(","sample_features",",","neighbor_features",",","neighbor_weights",")","of","tensors",".","See","`","utils.unpack_neighbor_features","`","for","a","detailed","description","."]
code: def call(self, inputs, keep_rank=True):
      """Extracts neighbor features and weights from a dictionary of inputs.
  
      This function is a wrapper around `utils.unpack_neighbor_features`. See
      `utils.unpack_neighbor_features` for documentation on the expected input
      format and return values.
  
      Args:
        inputs: Dictionary of `tf.Tensor` features with keys for neighbors and
          weights described by `neighbor_config`.
        keep_rank: Boolean indicating whether each value of `neighbor_features`
          retains the rank from the corresponding value in `sample_features`
          by merging the neighborhood size with the batch_size dimension, or
          contains an extra neighborhood dimension at axis 1. Defaults to `True`.
  
      Returns:
        A tuple (sample_features, neighbor_features, neighbor_weights) of tensors.
        See `utils.unpack_neighbor_features` for a detailed description.
      """
      return utils.unpack_neighbor_features(
          inputs, self._neighbor_config, keep_rank=keep_rank
      )
  
file_path: neural-structured-learning/neural_structured_learning/keras/layers/neighbor_features.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import attr
  import neural_structured_learning.configs
  from neural_structured_learning.lib import utils
  import tensorflow
label_desc: Use image node_id 2 for calling the NeighborFeatures obj's underlying member method code with example usage: obj.call(inputs, keep_rank) and returns: utils