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.pytorch.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
      with self.name_scope():
          self.fc = nn.Sequential()
          for _ in range(k):
              self.fc.add(
                  nn.Dense(
                      out_feats,
                      in_units=in_feats,
                      use_bias=False,
                      weight_initializer=mx.init.Xavier(
                          magnitude=math.sqrt(2.0)
                      ),
                  )
              )
          if bias:
              self.bias = self.params.get(
                  "bias", shape=(out_feats,), init=mx.init.Zero()
              )
          else:
              self.bias = None
  
file_path: dgl/python/dgl/nn/mxnet/conv/densechebconv.py
imports: import math
  import mxnet
  from mxnet import nd
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new DenseChebConv object from inherited base classes: nn with example: obj = DenseChebConv(in_feats, out_feats, k, bias)