0x0000, 0x0110, 0x0000, 0x0112, 0x0000, 0x0114, 0x0114, 0x0000,
	0x0117, 0x0117, 0x0117, 0x0117, 0x0000, 0x011c, 0x0000, 0x011e,
	0x0000, 0x0120, 0x0120, 0x0000, 0x0123, 0x0123, 0x0123, 0x0123,
	0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123,
	0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123,
	0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123, 0x0123,
	// Entry 140 - 17F

}

func.func @annotateInferenceType(%arg0: tensor<1x1x384x!quant.uniform<i8:f32, 0.003:-128>>) -> tensor<1x384x384x!quant.uniform<i8:f32, 0.003:-128>>{
  // CHECK-NOT: tac.device tac.inference_type
  %0 = "tfl.pseudo_qconst"() {qtype = tensor<1x384x1x!quant.uniform<i8:f32, 0.003:-128>>, value = dense<127> : tensor<1x384x1xi8>} : () -> tensor<1x384x1x!quant.uniform<i8:f32, 0.003:-128>>
  // CHECK: tac.device = "CPU", tac.inference_type = "QUANTIZED_INT8"

)

// Floating-point limit values.
// Max is the largest finite value representable by the type.
// SmallestNonzero is the smallest positive, non-zero value representable by the type.
const (
	MaxFloat32             = 0x1p127 * (1 + (1 - 0x1p-23)) // 3.40282346638528859811704183484516925440e+38
	SmallestNonzeroFloat32 = 0x1p-126 * 0x1p-23            // 1.401298464324817070923729583289916131280e-45