// -----

// CHECK-LABEL: func @fft_1D
func.func @fft_1D(%arg0: tensor<8xcomplex<f32>>) -> tensor<8xcomplex<f32>> {
  // CHECK: "mhlo.fft"(%arg0) <{fft_length = dense<8> : tensor<1xi64>, fft_type = #mhlo<fft_type FFT>}> : (tensor<8xcomplex<f32>>
  %0 = "tf.FFT"(%arg0) : (tensor<8xcomplex<f32>>) -> tensor<8xcomplex<f32>>
  func.return %0 : tensor<8xcomplex<f32>>
}

// -----

// CHECK-LABEL: func @ifft_1D

    RankedTensorType act_type = mlir::dyn_cast<RankedTensorType>(act.getType());
    if (!act_type) return failure();
    Type act_ele_type = act_type.getElementType();
    // To support mixed precision, the statistics type, which maybe more
    // precise than the input types, are used for this op.
    Type kernel_type = mlir::cast<TensorType>(scale.getType()).getElementType();

		base.Fatalf("deferStructFnField is %q, not fn", name)
	}

	n := ir.NewDeclNameAt(src.NoXPos, ir.OTYPE, ir.Pkgs.Runtime.Lookup("_defer"))
	typ := types.NewNamed(n)
	n.SetType(typ)
	n.SetTypecheck(1)

	// build struct holding the above fields
	typ.SetUnderlying(types.NewStruct(fields))
	types.CalcStructSize(typ)

	deferType = typ
	return typ
}