"comma separated list of allowlisted functions to be quantized. Only used in tests">,
      Option<"quantize_signed_", "quantize-signed", "bool", "false",
             "signed inference type. Only used in tests">,
      Option<"activation_number_of_bits_", "activation-number-of-bits", "int", "8",
             "number of bits for inference type. Only used in tests">,

<{host_mlir_module = "#loc1 = loc(\22Reshape:\22)\0A#loc2 = loc(\22Reshape_4\22)\0A#loc3 = loc(\22Reshape\22)\0A#loc9 = loc(fused[#loc1, #loc2, #loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, tensor<1x1120x?xi32>, tensor<1120x?xi32>, tensor<2xi32>) {\0A    %0 = \...

	return b0[idx] * b1
}

func indexStore(b0 []float64, b1 float64, idx int) {
	// arm64:`FMOVD\sF[0-9]+,\s\(R[0-9]+\)\(R[0-9]+<<3\)`
	b0[idx] = b1
}

// ----------- //
//    Fused    //
// ----------- //

func FusedAdd32(x, y, z float32) float32 {
	// s390x:"FMADDS\t"
	// ppc64x:"FMADDS\t"
	// arm64:"FMADDS"
	// riscv64:"FMADDS\t"
	return x*y + z
}

    metadata("1. Artifact metadata: hash, name\n2. Dependencies instrumented type hierarchy")
    result("1. Transformed jar\n2. hash of original jar")
    buildService1[["Build service 1:\nused to provide type hierarchies of \ndependencies"]]
    buildService2[["Build service 2:\nused to get original Jar\nvia hash from original classpath"]]
    jar --> collect --> artifactOutput --> merge --> metadata --> instrument --> result

// CHECK: return %[[CONCAT]] : tensor<12xf32>
}

// -----

// Tests a case where the ConstOp's location is a fused loc containing more
// than two strings to be combined to form the shared_name. It must not contain
// the character ";" (which is often used as a delimiter to join fused loc's
// items).

module attributes {tf_saved_model.semantics} {
// CHECK: func.func @init_func_restore_op()

    } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<1x10xf32>  loc(fused["MatMul:", "test_opt_out"])
    %2 = "tf.Reshape"(%1, %cst_0) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<?x10xf32> loc(fused["Reshape:", "model/reshape"])
    %3 = "tf.AddV2"(%2, %cst) : (tensor<?x10xf32>, tensor<10xf32>) -> tensor<?x10xf32> loc(fused["AddV2:", "model/add"])
    func.return %3 : tensor<?x10xf32>
  }
}

    "mlir::stablehlo::StablehloDialect",
  ];
}

def UnwrapXlaCallModuleOpPass : Pass<"stablehlo-unwrap-xla-call-module-op", "ModuleOp"> {
  let summary = "Unwrap XlaCallModuleOps into inline functions if not used for quantizing fused patterns.";
  let dependentDialects = ["TF::TensorFlowDialect"];
}

def ConvertFuncToBfloat16Pass : Pass<"stablehlo-convert-func-to-bfloat16", "mlir::func::FuncOp"> {

}

# TODO(b/142400497): What is the semantic contract for locations?
# CHECK: "tf.Const"{{.*}}value = dense<2>{{.*}}loc(fused["Const:",  "n1@f1", "n2@f2", "fused_node_outside_function"])
# CHECK: "tf.Const"{{.*}}value = dense<0>{{.*}}loc(fused["Const:", "node_outside_function"])

			{0.29711226, 0.89436173, 0.097454615, 0.36318043}, // fused multiply-add result: 0.36318046
			{0.6810783, 0.24151509, 0.31152245, 0.47601312},   // fused multiply-add result: 0.47601315
			{0.73023146, 0.18292491, 0.4283571, 0.5619346},    // fused multiply-add result: 0.56193465
			{0.89634174, 0.32208398, 0.7211478, 1.009845},     // fused multiply-add result: 1.0098451

<{host_mlir_module = "#loc1 = loc(\22Reshape:\22)\0A#loc2 = loc(\22Reshape_4\22)\0A#loc3 = loc(\22Reshape\22)\0A#loc9 = loc(fused[#loc1, #loc2, #loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, tensor<1x1120x?xi32>, tensor<1120x?xi32>, tensor<2xi32>) {\0A    %0 = \...