code: def symbolic(g: torch.Graph, input: torch.Value, bias: torch.Value):
      # define constants and variables
      x = g.op("Add", input, bias)
      const_1 = g.op(
          "Constant", value_t=torch.tensor(0.5, dtype=torch.float16)
      )
      const_2 = g.op(
          "Constant", value_t=torch.tensor(1.0, dtype=torch.float16)
      )
      const_3 = g.op(
          "Constant",
          value_t=torch.tensor(0.79788456, dtype=torch.float16),
      )
      const_4 = g.op(
          "Constant",
          value_t=torch.tensor(0.044715, dtype=torch.float16),
      )
  
      # calculates (1 + 0.044715 * x * x)
      p_1 = g.op(
          "Add", const_2, g.op("Mul", x, g.op("Mul", const_4, x))
      )
  
      # calculates torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
      p_2 = g.op("Tanh", g.op("Mul", const_3, g.op("Mul", x, p_1)))
  
      # calculates x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
      return g.op(
          "Mul", const_1, g.op("Mul", x, g.op("Add", const_2, p_2))
      )
  
file_path: NeMo/nemo/collections/nlp/modules/common/megatron/fused_bias_gelu.py
imports: import torch
label_desc: Use image node_id 3 for calling the GeLUFunction obj's underlying member method code with example usage: obj.symbolic(g, input, bias) and returns: g