class_docstr: Base class for GraphSAGE aggregators
  
  Args:
      output_dim (int): Output dimension
      bias (bool): Optional flag indicating whether (True) or not (False; default)
          a bias term should be included.
      act (Callable or str): name of the activation function to use (must be a
          Keras activation function), or alternatively, a TensorFlow operation.
      kernel_initializer (str or func): The initialiser to use for the weights
      kernel_regularizer (str or func): The regulariser to use for the weights
      kernel_constraint (str or func): The constraint to use for the weights
      bias_initializer (str or func): The initialiser to use for the bias
      bias_regularizer (str or func): The regulariser to use for the bias
      bias_constraint (str or func): The constraint to use for the bias
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code: def __init__(
      self,
      output_dim: int = 0,
      bias: bool = False,
      act: Union[Callable, AnyStr] = "relu",
      kernel_initializer="glorot_uniform",
      kernel_regularizer=None,
      kernel_constraint=None,
      bias_initializer="zeros",
      bias_regularizer=None,
      bias_constraint=None,
      **kwargs,
  ):
      self.output_dim = output_dim
      self.has_bias = bias
      self.act = activations.get(act)
      super().__init__(**kwargs)
  
      self.kernel_initializer = initializers.get(kernel_initializer)
      self.kernel_regularizer = regularizers.get(kernel_regularizer)
      self.kernel_constraint = constraints.get(kernel_constraint)
      self.bias_initializer = initializers.get(bias_initializer)
      self.bias_regularizer = regularizers.get(bias_regularizer)
      self.bias_constraint = constraints.get(bias_constraint)
  
      # These will be filled in at build time
      self.bias = None
      self.w_self = None
      self.w_group = None
      self.weight_dims = None
      self.included_weight_groups = None
  
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 GraphSAGEAggregator object from inherited base classes: Layer with example: obj = GraphSAGEAggregator(output_dim, bias, act, kernel_initializer, kernel_regularizer, kernel_constraint, bias_initializer, bias_regularizer, bias_constraint)