code: def __init__(
      self,
      num_bases,
      num_rel,
      num_ent,
      in_dim,
      layer_size,
      comp_fn="sub",
      batchnorm=True,
      dropout=0.1,
      layer_dropout=[0.3],
      num_filt=200,
      hid_drop=0.3,
      feat_drop=0.3,
      ker_sz=5,
      k_w=5,
      k_h=5,
  ):
      super(CompGCN_ConvE, self).__init__()
  
      self.embed_dim = layer_size[-1]
      self.hid_drop = hid_drop
      self.feat_drop = feat_drop
      self.ker_sz = ker_sz
      self.k_w = k_w
      self.k_h = k_h
      self.num_filt = num_filt
  
      # compGCN model to get sub/rel embs
      self.compGCN_Model = CompGCN(
          num_bases,
          num_rel,
          num_ent,
          in_dim,
          layer_size,
          comp_fn,
          batchnorm,
          dropout,
          layer_dropout,
      )
  
      # batchnorms to the combined (sub+rel) emb
      self.bn0 = th.nn.BatchNorm2d(1)
      self.bn1 = th.nn.BatchNorm2d(self.num_filt)
      self.bn2 = th.nn.BatchNorm1d(self.embed_dim)
  
      # dropouts and conv module to the combined (sub+rel) emb
      self.hidden_drop = th.nn.Dropout(self.hid_drop)
      self.feature_drop = th.nn.Dropout(self.feat_drop)
      self.m_conv1 = th.nn.Conv2d(
          1,
          out_channels=self.num_filt,
          kernel_size=(self.ker_sz, self.ker_sz),
          stride=1,
          padding=0,
          bias=False,
      )
  
      flat_sz_h = int(2 * self.k_w) - self.ker_sz + 1
      flat_sz_w = self.k_h - self.ker_sz + 1
      self.flat_sz = flat_sz_h * flat_sz_w * self.num_filt
      self.fc = th.nn.Linear(self.flat_sz, self.embed_dim)
  
      # bias to the score
      self.bias = nn.Parameter(th.zeros(num_ent))
  
file_path: dgl/examples/pytorch/compGCN/models.py
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from utils import ccorr
label_desc: Use image node_id 1 to create a new CompGCN_ConvE object from inherited base classes: nn with example: obj = CompGCN_ConvE(num_bases, num_rel, num_ent, in_dim, layer_size, comp_fn, batchnorm, dropout, layer_dropout, num_filt, hid_drop, feat_drop, ker_sz, k_w, k_h)