code: def bias_add_activation(C, bias=None, activation=""):
      if bias is not None:
          C += bias
      # activation
      if activation == "relu":
          relu = torch.nn.Relu()
          C = relu(C)
      elif activation == "leaky_relu":
          leaky_relu = torch.nn.LeakyReLU(0.01)
          C = leaky_relu(C)
      elif activation == "gelu":
          sigmoid = torch.nn.Sigmoid()
          C = sigmoid(1.702 * C) * C
      elif activation == "sigmoid":
          sigmoid = torch.nn.Sigmoid()
          C = sigmoid(C)
      return C
  
imports: import torch
  import triton
  import os
  from filelock import FileLock
  import deepspeed.ops.transformer.inference.triton.triton_matmul_kernel
  import pickle
  from io import open
  import deepspeed
  from pathlib import Path
  import atexit
label_desc: Use image node_id 2 for calling a global function with example usage: bias_add_activation(C, bias, activation) and returns: C