code: def _matching_fc(
      tensor,
      name,
      size,
      nsteps,
      init_scale,
      init_bias,
      np_mask,
      is_act_model,
  ):
      """
      Add fc op, and add mask op when not in action mode
      """
      if tensor.shape[-1] == size:
          assert False
          return tensor
      else:
          mask = tf.get_variable(
              "act_mask",
              dtype=tf.float32,
              initializer=np_mask[0],
              trainable=False,
          )
          mask_npinf = tf.get_variable(
              "act_mask_npinf",
              dtype=tf.float32,
              initializer=np_mask[1],
              trainable=False,
          )
          res = fc(
              tensor,
              name,
              size,
              init_scale=init_scale,
              init_bias=init_bias,
          )
          if not is_act_model:
              re_res = tf.reshape(res, [-1, nsteps, size])
              masked_res = tf.math.multiply(re_res, mask)
              re_masked_res = tf.reshape(masked_res, [-1, size])
              return re_masked_res, mask, mask_npinf
          else:
              return res, mask, mask_npinf
  
imports: import tensorflow.compat.v1
  from .util import fc
label_desc: Use image node_id 1 for calling a global function with example usage: _matching_fc(tensor, name, size, nsteps, init_scale, init_bias, np_mask, is_act_model) and returns: tensor, re_masked_res,  mask,  mask_npinf, res,  mask,  mask_npinf