class_docstr: The DeepDTA's CNN encoder module, which comprises three 1D-convolutional layers and one max-pooling layer.
  The module is applied to encoding drug/target sequence information, and the input should be transformed information
  with integer/label encoding. The original paper is `"DeepDTA: deep drug–target binding affinity prediction"
  <https://academic.oup.com/bioinformatics/article/34/17/i821/5093245>`_ .
  
  Args:
      num_embeddings (int): Number of embedding labels/categories, depends on the types of encoding sequence.
      embedding_dim (int): Dimension of embedding labels/categories.
      sequence_length (int): Max length of input sequence.
      num_kernels (int): Number of kernels (filters).
      kernel_length (int): Length of kernel (filter).
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code: def forward(self, x):
      x = self.embedding(x)
      x = F.relu(self.conv1(x))
      x = F.relu(self.conv2(x))
      x = F.relu(self.conv3(x))
      x = self.global_max_pool(x)
      x = x.squeeze(2)
      return x
  
file_path: pykale/kale/embed/seq_nn.py
imports: import torch.nn
  import torch.nn.functional
  from torch_geometric.nn import GCNConv, global_max_pool
label_desc: Use image node_id 2 for calling the CNNEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: x