class_docstr: This implements the graph attention as in Watch Your Step: Learning Node Embeddings via Graph Attention
  https://arxiv.org/pdf/1710.09599.pdf.
  
  Args:
      walk_length (int): the length of the random walks. Equivalent to the number of adjacency powers used. Defaults
          to `10` as this value was found to perform well by the authors of the paper.
      attention_initializer (str or func, optional): The initialiser to use for the attention weights.
      attention_regularizer (str or func, optional): The regulariser to use for the attention weights.
      attention_constraint (str or func, optional): The constraint to use for the attention weights.
      input_dim (tuple of int, optional): The shape of the input to the layer.
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code: def get_config(self):
      config = {
          "walk_length": self.walk_length,
          "attention_initializer": initializers.serialize(
              self.attention_initializer
          ),
          "attention_regularizer": regularizers.serialize(
              self.attention_regularizer
          ),
          "attention_constraint": constraints.serialize(
              self.attention_constraint
          ),
      }
      base_config = super().get_config()
      return {**base_config, **config}
  
imports: import tensorflow
  from tensorflow.keras.layers import Layer, Embedding, Input, Lambda, Concatenate, Dense
  from tensorflow.keras import backend
  from tensorflow.keras import initializers, constraints, regularizers
  import numpy
  import warnings
  from ..mapper.adjacency_generators import AdjacencyPowerGenerator
  from ..core.validation import require_integer_in_range
  from .misc import deprecated_model_function
label_desc: Use image node_id 2 for calling the AttentiveWalk obj's underlying member method code with example usage: obj.get_config() and returns: dict