code: def get_dense_bias_gelu(M, N, K, out_dtype="float16"):
      bias_add = get_dense_bias(M, N, K, out_dtype)
      mul = bias_add * relay.const(
          (1.0 / math.sqrt(2.0)), dtype=out_dtype
      )
      if out_dtype == "float16":
          erf = relay.cast(
              relay.op.erf(relay.cast(mul, "float32")), "float16"
          )
      else:
          erf = relay.op.erf(mul)
      mul_half = erf * relay.const(0.5, dtype=out_dtype)
      add = mul_half + relay.const(0.5, dtype=out_dtype)
      return add * bias_add
  
imports: import logging
  import tempfile
  import math
  import tvm
  from tvm import relay
  from tvm.contrib.cudnn import conv_output_shape
  import numpy
  from tvm.relay import op
  from tvm.runtime.vm import VirtualMachine
  from tvm.relay.op.contrib.cutlass import partition_for_cutlass
  from tvm import auto_scheduler
  from tvm.relay.transform import FirstOrderGradient, ToMixedPrecision, InferType
  from tvm.contrib.cutlass import has_cutlass, num_cutlass_partitions, finalize_modules, finalize_modules_vm
  import tvm.testing
label_desc: Use image node_id 10 for calling a global function with example usage: get_dense_bias_gelu(M, N, K, out_dtype) and returns: unknown