"""

        expect:
        resolve() == [header]
    }

    def "can produce a macro function call by concatenating name and args that contain token concatenation"() {
        given:
        sourceFile << """
            #define HEADER_3 "hello.h"
            #define FUNC(X, Y) X
            #define CONCAT_(X, Y) X ## Y

  %0 = arith.constant dense<1.0> : tensor<1x2xf32>
  %1 = "tfl.concatenation"(%arg0, %0) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<2x2xf32>
  func.return %1 : tensor<2x2xf32>

// CHECK-NEXT: %[[cst:.*]] = arith.constant dense<1.000000e+00> : tensor<1x2xf32>
// CHECK-NEXT: %[[cc:.*]] = "tfl.concatenation"(%arg0, %[[cst]])
// CHECK-NEXT: return %[[cc]]
}

     */
    EXPRESSIONS,
    /**
     * A token concatenation expression whose value should not be macro expanded. These appear in the body of macro directives or as the arguments to a {@link #MACRO_FUNCTION} eg {@code #define ABC(X, Y) X ## Y}.
     */
    TOKEN_CONCATENATION,
    /**
     * A token concatenation expression whose value should be macro expanded.
     */
    EXPAND_TOKEN_CONCATENATION,

    %22 = "tfl.concatenation"(%cst_6, %21) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1xi32>, tensor<?xi32>) -> tensor<?xi32>
    %23 = "tfl.reshape"(%arg2, %cst_12) : (tensor<i32>, tensor<1xi32>) -> tensor<1xi32>
    %24 = "tfl.fill"(%cst_11, %cst_9) : (tensor<1xi32>, tensor<i32>) -> tensor<?xi32>

	// to pass a dst bigger than src, and in that case, XORKeyStream will
	// only update dst[:len(src)] and will not touch the rest of dst.
	//
	// Multiple calls to XORKeyStream behave as if the concatenation of
	// the src buffers was passed in a single run. That is, Stream
	// maintains state and does not reset at each XORKeyStream call.
	XORKeyStream(dst, src []byte)
}

monitoring::GaugeCell<std::string>& SavedModelWritePath();

// Returns "/tensorflow/core/saved_model/write/path_and_fingerprint" cell, which
// contains the path (saved_model_path) and fingerprint (concatenation of
// graph_def_program_hash, signature_def_hash, saved_object_graph_hash,
// and checkpoint_hash) of the SavedModel when it is exported.
monitoring::GaugeCell<std::string>& SavedModelWritePathAndSingleprint();

 %0 = "tfl.concatenation"(%arg0, %arg1, %arg2, %arg3) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<384x384xf32>, tensor<384x384xf32>, tensor<384x384xf32>, tensor<384x384xf32>) -> tensor<?x?xf32>
  func.return %0: tensor<?x?xf32>
}

// CHECK-NOT: "tfl.reshape"

// (concatenation of graph_def_program_hash, signature_def_hash,
// saved_object_graph_hash, and checkpoint_hash) of the newly written
// SavedModel.
auto* saved_model_write_path_and_singleprint =
    monitoring::Gauge<std::string, 0>::New(
        "/tensorflow/core/saved_model/write/path_and_singleprint",
        "The path (saved_model_path) and singleprint (concatenation of "

  %1 = "tfl.concatenation"(%0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1000x1000xf32>, tensor<1000x1000xf32>) -> tensor<2000x1000xf32>
  func.return %1 : tensor<2000x1000xf32>

// CHECK-NEXT: tfl.dequantize
// CHECK-NEXT: tfl.concatenation
}

// CHECK-LABEL: no_pushdown_multiple_outputs

// CHECK:  [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
// CHECK:  [[VAL_2:%.*]] = "tfl.concatenation"([[VAL_0]], [[VAL_1]]) <{axis = 3 : i32, fused_activation_function = "NONE"}> {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x2xf32>