code: def fused_gemm_gelu(
      input,
      weight,
      weight_scale,
      bias,
      weight_out,
      weight_out_scale,
      epsilon,
      pre_layer_norm,
      q_int8,
      async_op,
      transposed_mode,
      use_triton_ln=True,
  ):
      assert not transposed_mode
  
      # activation
      activation = "gelu"
  
      # intermediate fc in FF
      intm_out = matmul_ext.matmul(
          input,
          weight,
          bias=bias,
          activation=activation,
          use_triton=True,
      )
  
      # output fc in FF
      ff_out = matmul_ext.matmul(
          intm_out,
          weight_out,
          bias=None,
          activation="",  # bias added layer with residual_add + bias + layerNorm layer
          use_triton=True,
      )
      return ff_out
  
file_path: deepspeed/deepspeed/ops/transformer/inference/triton/ops.py
imports: import deepspeed
  from deepspeed.ops.op_builder import InferenceBuilder
  import deepspeed.ops.transformer.inference.triton.matmul_ext
  from deepspeed.ops.transformer.inference.triton.layer_norm import layer_norm, layer_norm_residual
label_desc: Use image node_id 2 for calling a global function with example usage: fused_gemm_gelu(input, weight, weight_scale, bias, weight_out, weight_out_scale, epsilon, pre_layer_norm, q_int8, async_op, transposed_mode, use_triton_ln) and returns: ff_out