code: def Linear(input_size, hidden_size, with_bias=True):
      fan_in = input_size
      bias_bound = 1.0 / math.sqrt(fan_in)
      fc_bias_attr = paddle.ParamAttr(
          initializer=nn.initializer.Uniform(
              low=-bias_bound, high=bias_bound
          )
      )
  
      negative_slope = math.sqrt(5)
      gain = math.sqrt(2.0 / (1 + negative_slope**2))
      std = gain / math.sqrt(fan_in)
      weight_bound = math.sqrt(3.0) * std
      fc_w_attr = paddle.ParamAttr(
          initializer=nn.initializer.Uniform(
              low=-weight_bound, high=weight_bound
          )
      )
  
      if not with_bias:
          fc_bias_attr = False
  
      return nn.Linear(
          input_size,
          hidden_size,
          weight_attr=fc_w_attr,
          bias_attr=fc_bias_attr,
      )
  
imports: import numpy
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddle.distributed
  import pgl
  import pgl.nn
  from pgl.nn import functional
  from pgl.utils.logger import log
  import models.mol_encoder
label_desc: Use image node_id 2 for calling a global function with example usage: Linear(input_size, hidden_size, with_bias) and returns: nn