class_docstr: Max Pooling Aggregator for GraphSAGE implemented with Keras base layer
  
  Implements the aggregator of Eq. (3) in Hamilton et al. (2017)
  
  Args:
      output_dim (int): Output dimension
      bias (bool): Optional bias
      act (Callable or str): name of the activation function to use (must be a
          Keras activation function), or alternatively, a TensorFlow operation.
class_docstr_tok: ["Max","Pooling","Aggregator","for","GraphSAGE","implemented","with","Keras","base","layer","Implements","the","aggregator","of","Eq",".","(","3",")","in","Hamilton","et","al",".","(","2017",")","Args",":","output_dim","(","int",")",":","Output","dimension","bias","(","bool",")",":","Optional","bias","act","(","Callable","or","str",")",":","name","of","the","activation","function","to","use","(","must","be","a","Keras","activation","function",")",",","or","alternatively",",","a","TensorFlow","operation","."]
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
      # TODO: These should be user parameters
      self.hidden_dim = self.output_dim
      self.hidden_act = activations.get("relu")
  
imports: import warnings
  import numpy
  from tensorflow.keras.layers import Layer
  from tensorflow.keras import Input
  from tensorflow.keras import backend
  from tensorflow.keras.layers import Lambda, Dropout, Reshape, LeakyReLU
  from tensorflow.keras.utils import Sequence
  from tensorflow.keras import activations, initializers, constraints, regularizers
  from typing import List, Tuple, Callable, AnyStr, Union
  from ..mapper import GraphSAGENodeGenerator, GraphSAGELinkGenerator, DirectedGraphSAGENodeGenerator, DirectedGraphSAGELinkGenerator, NodeSequence, LinkSequence
  from .misc import deprecated_model_function
  from ..connector.neo4j.mapper import Neo4jGraphSAGENodeGenerator, Neo4jDirectedGraphSAGENodeGenerator
label_desc: Use image node_id 1 to create a new MaxPoolingAggregator object from inherited base classes: GraphSAGEAggregator with example: obj = MaxPoolingAggregator()