code: def inference_net(self, C, reuse=None):
      # input
      # h = tf.nn.relu(tf.sparse_matmul(C, self.inference_params[0], a_is_sparse=True))
      h = tf.nn.relu(tf.matmul(C, self.inference_params[0]))
      # intermediate hidden layer
      for l in range(1, self.L):
          # h = tf.nn.relu(tf.sparse_matmul(h, self.inference_params[l], a_is_sparse=True))
          h = tf.nn.relu(tf.matmul(h, self.inference_params[l]))
  
      # output
      # beta = tf.nn.softplus(tf.matmul(h, self.inference_params[self.L], a_is_sparse=True)) + 0.3
      # alpha = tf.nn.softplus(tf.matmul(h, self.inference_params[self.L + 1], a_is_sparse=True)) + 0.3
      beta = (
          tf.nn.softplus(tf.matmul(h, self.inference_params[self.L]))
          + 0.3
      )
      alpha = (
          tf.nn.softplus(
              tf.matmul(h, self.inference_params[self.L + 1])
          )
          + 0.3
      )
  
      return alpha, beta
  
file_path: cornac/cornac/models/pcrl/pcrl.py
imports: import numpy
  import scipy.sparse
  import scipy
  import tensorflow
label_desc: Use image node_id 12 for calling the PCRL_ obj's underlying member method code with example usage: obj.inference_net(C, reuse) and returns: alpha,  beta