code: def test_broadcast_layout():
      # Test broadcast support for NHWC layout.
      data_shape = (1, 229, 229, 3)  # NHWC
      data_dtype = "uint8"
      kernel_shape = (7, 7, 64, 3)  # HWOI
      kernel_dtype = "int8"
      _, qnn_func = get_funcs(
          data_shape=data_shape,
          data_dtype=data_dtype,
          kernel_shape=kernel_shape,
          kernel_dtype=kernel_dtype,
          input_zero_point=8,
          kernel_zero_point=3,
          input_scale=1.0,
          kernel_scale=1.0,
          kernel_size=(7, 7),
          padding=(1, 1),
          strides=(1, 1),
          dilation=(1, 1),
          data_layout="NHWC",
          kernel_layout="HWOI",
          out_dtype="int32",
      )
      func = qnn_func["main"].body
      bias = relay.var("bias", shape=(64,), dtype="int32")
      bias2 = relay.var("bias2", shape=(1, 233, 233, 64), dtype="int32")
  
      # Check broadcast support on both lhs and rhs
      func = relay.add(func, bias2)
      func = relay.add(bias2, func)
      func = relay.add(bias, func)
      func = relay.add(func, bias)
      func = relay.Function(relay.analysis.free_vars(func), func)
      mod = tvm.IRModule.from_expr(func)
      with tvm.transform.PassContext(opt_level=3):
          libs = relay.build(
              mod, "llvm -mtriple=x86_64-linux-gnu -mcpu=skylake-avx512"
          )
  
imports: import numpy
  import tvm
  from tvm import relay, te
  from tvm.contrib import graph_executor
  from tvm.relay import transform
  from tvm.relay.testing import run_infer_type
  from tvm.relay.testing.temp_op_attr import TempOpAttr
label_desc: Use image node_id 13 for calling a global function with example usage: test_broadcast_layout() without return types