Hi Liu,
I did as you say, but it did show dimension mismatch:

  File "/home/yubi/practice/GCN_RL_Circuit_Project/circuit_rl_experiment/stage5_gcn_ddpg/main_circuit_gcn_ddpg.py", line 85, in <module>
    agent.learn(normalized_adj)

  File "/home/yubi/practice/GCN_RL_Circuit_Project/circuit_rl_experiment/stage5_gcn_ddpg/gcn_ddpg_agent.py", line 221, in learn
    target_actions = self.target_actor.forward(states_, adj)

  File "/home/yubi/practice/GCN_RL_Circuit_Project/circuit_rl_experiment/stage5_gcn_ddpg/gcn_ddpg_agent.py", line 61, in forward
    x = self.gc1(x, adj)

  File "/home/yubi/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)

  File "/home/yubi/practice/GCN_RL_Circuit_Project/circuit_rl_experiment/stage5_gcn_ddpg/gcn_layers.py", line 37, in forward
    support = torch.mm(self.weight, input)

RuntimeError: size mismatch, m1: [18 x 1], m2: [32 x 9] at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/THC/generic/THCTensorMathBlas.cu:290

and this is my GCN layer code:
import math

import torch

from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        # self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        self.weight = Parameter(torch.FloatTensor(out_features, in_features))
        if bias:
            # self.bias = Parameter(torch.FloatTensor(out_features))
            self.bias = Parameter(torch.FloatTensor(out_features, 1))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1. / math.sqrt(self.out_features)
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input, adj):
        #support = torch.mm(input, self.weight)
        #output = torch.spmm(adj, support)
        
        support = torch.mm(self.weight, input)
        output = torch.mm(support, torch.transpose(adj, 0, 1))
        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' \
               + str(self.in_features) + ' -> ' \
               + str(self.out_features) + ')'