code: def __init__(
      self,
      dim,
      K,
      num_layers,
      shared_weights=False,
      act=tf.nn.relu,
      **kwargs,
  ):
      super(ARMAConv, self).__init__(aggr="add", **kwargs)
      self.K = K
      self.T = num_layers
      self.shared_weights = shared_weights
      self.act = act
      self.dim = dim
      if self.shared_weights:
          self.ws = [tf.layers.Dense(self.K * dim, use_bias=False)]
          self.vs = [tf.layers.Dense(self.K * dim, use_bias=False)]
      else:
          self.ws = [
              tf.layers.Dense(self.K * dim, use_bias=False)
              for _ in range(self.T)
          ]
          self.vs = [
              tf.layers.Dense(self.K * dim, use_bias=False)
              for _ in range(self.T)
          ]
  
file_path: euler/tf_euler/python/convolution/arma_conv.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_euler.python.convolution import conv
  from tf_euler.python.euler_ops import mp_ops
label_desc: Use image node_id 1 to create a new ARMAConv object from inherited base classes: conv with example: obj = ARMAConv(dim, K, num_layers, shared_weights, act)