class_docstr: Aggregates via max-pooling over MLP functions.
      
class_docstr_tok: ["Aggregates","via","max-pooling","over","MLP","functions","."]
code: def __init__(
      self,
      input_dim,
      output_dim,
      model_size="small",
      neigh_input_dim=None,
      dropout=0.0,
      bias=False,
      act=tf.nn.relu,
      name=None,
      concat=False,
      **kwargs,
  ):
      super(MaxPoolingAggregator, self).__init__(**kwargs)
  
      self.dropout = dropout
      self.bias = bias
      self.act = act
      self.concat = concat
  
      if neigh_input_dim is None:
          neigh_input_dim = input_dim
  
      if name is not None:
          name = "/" + name
      else:
          name = ""
  
      if model_size == "small":
          hidden_dim = self.hidden_dim = 512
      elif model_size == "big":
          hidden_dim = self.hidden_dim = 1024
  
      self.mlp_layers = []
      self.mlp_layers.append(
          Dense(
              input_dim=neigh_input_dim,
              output_dim=hidden_dim,
              act=tf.nn.relu,
              dropout=dropout,
              sparse_inputs=False,
              logging=self.logging,
          )
      )
  
      with tf.variable_scope(self.name + name + "_vars"):
          self.vars["neigh_weights"] = glorot(
              [hidden_dim, output_dim], name="neigh_weights"
          )
  
          self.vars["self_weights"] = glorot(
              [input_dim, output_dim], name="self_weights"
          )
          if self.bias:
              self.vars["bias"] = zeros([self.output_dim], name="bias")
  
      if self.logging:
          self._log_vars()
  
      self.input_dim = input_dim
      self.output_dim = output_dim
      self.neigh_input_dim = neigh_input_dim
  
imports: import tensorflow
  from .layers import Layer, Dense
  from .inits import glorot, zeros
label_desc: Use image node_id 1 to create a new MaxPoolingAggregator object from inherited base classes: Layer with example: obj = MaxPoolingAggregator(input_dim, output_dim, model_size, neigh_input_dim, dropout, bias, act, name, concat)