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>`__
  
  .. math::
      h_i^{l+1} &= \sum_{k=0}^{K-1} W^{k, l}z_i^{k, l}
  
      Z^{0, l} &= H^{l}
  
      Z^{1, l} &= \tilde{L} \cdot H^{l}
  
      Z^{k, l} &= 2 \cdot \tilde{L} \cdot Z^{k-1, l} - Z^{k-2, l}
  
      \tilde{L} &= 2\left(I - \tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2}\right)/\lambda_{max} - I
  
  where :math:`\tilde{A}` is :math:`A` + :math:`I`, :math:`W` is learnable weight.
  
  Parameters
  ----------
  in_feats: int
      Dimension of input features; i.e, the number of dimensions of :math:`h_i^{(l)}`.
  out_feats: int
      Dimension of output features :math:`h_i^{(l+1)}`.
  k : int
      Chebyshev filter size :math:`K`.
  activation : function, optional
      Activation function. Default ``ReLu``.
  bias : bool, optional
      If True, adds a learnable bias to the output. Default: ``True``.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import tensorflow as tf
  >>> from dgl.nn import ChebConv
  >>> with tf.device("CPU:0"):
  ...     g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
  ...     feat = tf.ones((6, 10))
  ...     conv = ChebConv(10, 2, 2)
  ...     res = conv(g, feat)
  ...     res
  <tf.Tensor: shape=(6, 2), dtype=float32, numpy=
  array([[ 0.6163, -0.1809],
          [ 0.6163, -0.1809],
          [ 0.6163, -0.1809],
          [ 0.9698, -1.5053],
          [ 0.3664,  0.7556],
          [-0.2370,  3.0164]], dtype=float32)>
code: def __init__(
      self, in_feats, out_feats, k, activation=tf.nn.relu, bias=True
  ):
      super(ChebConv, self).__init__()
      self._k = k
      self._in_feats = in_feats
      self._out_feats = out_feats
      self.activation = activation
      self.linear = layers.Dense(out_feats, use_bias=bias)
  
file_path: dgl/python/dgl/nn/tensorflow/conv/chebconv.py
imports: import numpy
  import tensorflow
  from tensorflow.keras import layers
  from ....None import broadcast_nodes, function
  from ....base import dgl_warning
label_desc: Use image node_id 1 to create a new ChebConv object from inherited base classes: layers with example: obj = ChebConv(in_feats, out_feats, k, activation, bias)