// expected-error@+1 {{Denied unregistered attribute was found: denied_attr}}
  %0:2 = "tf.FusedN"(%arg0, %arg1, %arg2) {A=0:index, denied_attr} : (tensor<1x2x3x4x!tf_type.string>, tensor<f32>, tensor<f32>) -> (tensor<1x2x3x4x!tf_type.string>, tensor<f32>)
  func.return %0#1 : tensor<f32>

// CHECK-NEXT:   "tf.FusedN"(%arg0, %arg1, %arg2) {A = 0 : index, denied_attr}
}

// CHECK-LABEL: quantized_tensor

          contraction,
          "fused operation must be nested inside a function, If or While");
    }

    // If the contraction is used in multiple places, fusing it will only create
    // more contraction nodes, which is slower.
    if (!contraction.getResult().hasOneUse())
      return rewriter.notifyMatchFailure(contraction,
                                         "result is used by multiple ops");

  // DT_FLOAT, DT_HALF, DT_QINT8, and DT_QUINT8. When DT_HALF is used, the
  // `weight_quantization` flag needs to set to true. When DT_QUINT8 is used,
  // the `weight_quantization` flag needs to set to false.
  tensorflow::DataType inference_type = tensorflow::DT_FLOAT;

  // The input and output data type during inference. This flag is only used
  // when `inference_type` is different from DT_FLOAT. This flag can only be set

	HasAVX512IFMA       bool // Advanced vector extension 512 Integer Fused Multiply Add
	HasAVX512VBMI       bool // Advanced vector extension 512 Vector Byte Manipulation Instructions
	HasAVX5124VNNIW     bool // Advanced vector extension 512 Vector Neural Network Instructions Word variable precision
	HasAVX5124FMAPS     bool // Advanced vector extension 512 Fused Multiply Accumulation Packed Single precision

    // TFLite fused embedding_lookup op.
    ConvertEmbeddedLookupFunc convert_embedded_lookup(func);
    if (failed(convert_embedded_lookup.VerifySignature())) return;
    func.eraseBody();
    func.addEntryBlock();
    convert_embedded_lookup.RewriteFunc();
  } else if (attr.getValue() == mlir::TFL::kLstmCellSimple) {
    // Check if the lstm cell simple can be fused, if not, we just don't do
    // anything.

                 "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">,

    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()

    "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"> {

			{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