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 config(self):
      return {
          "stack_channels": self.stack_channels,
          "node_channels": self.node_channels,
          "edge_channels": self.edge_channels,
          "attention": self.attention,
          "node_activation": self.node_activation,
          "edge_activation": self.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 4 for calling the XENetConvBatch obj's underlying member method code with example usage: obj.config() and returns: dict