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``.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import torch as th
  >>> from dgl.nn import DenseChebConv
  >>>
  >>> feat = th.ones(6, 10)
  >>> adj = th.tensor([[0., 0., 1., 0., 0., 0.],
  ...         [1., 0., 0., 0., 0., 0.],
  ...         [0., 1., 0., 0., 0., 0.],
  ...         [0., 0., 1., 0., 0., 1.],
  ...         [0., 0., 0., 1., 0., 0.],
  ...         [0., 0., 0., 0., 0., 0.]])
  >>> conv = DenseChebConv(10, 2, 2)
  >>> res = conv(adj, feat)
  >>> res
  tensor([[-3.3516, -2.4797],
          [-3.3516, -2.4797],
          [-3.3516, -2.4797],
          [-4.5192, -3.0835],
          [-2.5259, -2.0527],
          [-0.5327, -1.0219]], grad_fn=<AddBackward0>)
  
  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
      self.W = nn.Parameter(th.Tensor(k, in_feats, out_feats))
      if bias:
          self.bias = nn.Parameter(th.Tensor(out_feats))
      else:
          self.register_buffer("bias", None)
      self.reset_parameters()
  
file_path: dgl/python/dgl/nn/pytorch/conv/densechebconv.py
imports: import torch
  from torch import nn
  from torch.nn import init
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)