class_docstr: A batch-mode version of XENetConv.
  
  **Mode**: batch.
  
  **This layer expects a dense adjacency matrix.**
class_docstr_tok: ["A","batch-mode","version","of","XENetConv",".","*","*","Mode","*","*",":","batch",".","*","*","This","layer","expects","a","dense","adjacency","matrix",".","*","*"]
code: def __init__(
      self,
      stack_channels,
      node_channels,
      edge_channels,
      attention: bool = True,
      node_activation=None,
      edge_activation=None,
      aggregate: str = "sum",
      use_bias: bool = True,
      kernel_initializer="glorot_uniform",
      bias_initializer="zeros",
      kernel_regularizer=None,
      bias_regularizer=None,
      activity_regularizer=None,
      kernel_constraint=None,
      bias_constraint=None,
      **kwargs,
  ):
      super().__init__(
          aggregate=aggregate,
          use_bias=use_bias,
          kernel_initializer=kernel_initializer,
          bias_initializer=bias_initializer,
          kernel_regularizer=kernel_regularizer,
          bias_regularizer=bias_regularizer,
          activity_regularizer=activity_regularizer,
          kernel_constraint=kernel_constraint,
          bias_constraint=bias_constraint,
          **kwargs,
      )
      self.stack_channels = stack_channels
      self.node_channels = node_channels
      self.edge_channels = edge_channels
      self.attention = attention
      self.node_activation = node_activation
      self.edge_activation = edge_activation
  
imports: from collections.abc import Iterable
  import tensorflow
  from tensorflow.keras.layers import Concatenate, Dense, Multiply, PReLU, ReLU
  from tensorflow.python.ops import gen_sparse_ops
  from spektral.layers.convolutional.conv import Conv
  from spektral.layers.convolutional.message_passing import MessagePassing
label_desc: Use image node_id 1 to create a new XENetConvBatch object from inherited base classes: Conv with example: obj = XENetConvBatch(stack_channels, node_channels, edge_channels, attention, node_activation, edge_activation, aggregate, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint)