class_docstr: Chebyshev Spectral Graph Convolution layer from `Convolutional
  Neural Networks on Graphs with Fast Localized Spectral Filtering
  <https://arxiv.org/pdf/1606.09375.pdf>`__
  
  We recommend to use this module when applying ChebConv on dense graphs.
  
  Parameters
  ----------
  in_feats: int
      Dimension of input features :math:`h_i^{(l)}`.
  out_feats: int
      Dimension of output features :math:`h_i^{(l+1)}`.
  k : int
      Chebyshev filter size.
  activation : function, optional
      Activation function, default is ReLu.
  bias : bool, optional
      If True, adds a learnable bias to the output. Default: ``True``.
  
  See also
  --------
  `ChebConv <https://docs.dgl.ai/api/python/nn.tensorflow.html#chebconv>`__
code: def __init__(self, in_feats, out_feats, k, bias=True):
      super(DenseChebConv, self).__init__()
      self._in_feats = in_feats
      self._out_feats = out_feats
      self._k = k
  
      # keras initializer assume last two dims as fan_in and fan_out
      xinit = tf.keras.initializers.glorot_normal()
      self.W = tf.Variable(
          initial_value=xinit(
              shape=(k, in_feats, out_feats), dtype="float32"
          ),
          trainable=True,
      )
  
      if bias:
          zeroinit = tf.keras.initializers.zeros()
          self.bias = tf.Variable(
              initial_value=zeroinit(
                  shape=(out_feats), dtype="float32"
              ),
              trainable=True,
          )
      else:
          self.bias = None
  
imports: import numpy
  import tensorflow
  from tensorflow.keras import layers
label_desc: Use image node_id 1 to create a new DenseChebConv object from inherited base classes: layers with example: obj = DenseChebConv(in_feats, out_feats, k, bias)