}

// -----

func.func @pack_CPU(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.device = "CPU", tac.interface_name = "func_2"} {
  // CHECK: tac.cost = 1.000000e+02
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", values_count = 2 : i32} : (tensor<100xf32>, tensor<100xf32>) -> tensor<2x100xf32>
  func.return %0 : tensor<2x100xf32>
}

		// instead of everytime testableNetwork is called.
		out, _ := exec.Command("oslevel", "-s").Output()
		if len(out) >= len("7200-XX-ZZ-YYMM") { // AIX 7.2, Tech Level XX, Service Pack ZZ, date YYMM
			aixVer := string(out[:4])
			tl, _ := strconv.Atoi(string(out[5:7]))
			unixEnabledOnAIX = aixVer > "7200" || (aixVer == "7200" && tl >= 2)
		}
	}
}

class TF_DerivedOperandTypeAttr<int idx> : DerivedTypeAttr<
  "return mlir::getElementTypeOrSelf(*getODSOperands(" # idx # ").begin());">;

// A derived attribute that returns the element types of the tensors in the
// actual value pack that corresponds to the `idx`-th ODS-declared variadic
// operand. This returns a list of element types so it is used for variadic
// operands that can have different element types.

        fileResolver.resolve('manifestFile') >> manifestFile

        and:
        // Means 'long russian text'
        String attributeValue = 'com.acme.example.pack.**, длинный.текст.на.русском.языке.**'
        DefaultManifest gradleManifest = new DefaultManifest(fileResolver)
        gradleManifest.getAttributes().put('Looong-Name-Of-Manifest-Entry', attributeValue)

        when:

    return 1 == (1 & (filter >> c));
  }

  // This is all essentially copied from ImmutableSet, but we have to duplicate because
  // of dependencies.

  // Represents how tightly we can pack things, as a maximum.
  private static final double DESIRED_LOAD_FACTOR = 0.5;

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing

git remote add origin https://vcs-test.swtch.com/git/README-only
mkdir .git/refs/remotes/origin
echo 'ref: refs/remotes/origin/master'
cp stdout .git/refs/remotes/origin/HEAD
unquote '# pack-refs with: peeled fully-peeled \n7f800d2ac276dd7042ea0e8d7438527d236fd098 refs/remotes/origin/master\n'
cp stdout .git/packed-refs
git branch --set-upstream-to=origin/master

git add pkg/pkg.go
at 2017-09-22T11:41:28-04:00

};
std::unique_ptr<TargetHardwareOperation> CreateArithmeticOp() {
  return std::make_unique<CpuArithmeticOp>();
}

// Currently used for these ops:
// tfl.concatenation / tfl.reshape / tfl.pack
class CpuConcatOp : public TargetHardwareOperation {
  double GetOpCost(mlir::Operation* op) const override {
    float cost = 0.0;
    int64_t count;

// go generate gen.go
// Code generated by the command above; DO NOT EDIT.

package hpack

var staticTable = &headerFieldTable{
	evictCount: 0,
	byName: map[string]uint64{
		":authority":                  1,
		":method":                     3,
		":path":                       5,
		":scheme":                     7,
		":status":                     14,
		"accept-charset":              15,
		"accept-encoding":             16,

      Multiply(context.get(), second_combined_value.get(),
               second_negative_one.get(), status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Un-pack the parallel tensor to verify that the operation was
  // successful. The resulting structure should be:
  //   second_device{first_device{1. * 3., 2. * 3.}, 3. * 3.}.
  std::array<TensorHandlePtr, 2> second_components;

  // CHECK: "tfl.pack"(%arg0, %arg1) <{axis = 0 : i32, values_count = 2 : i32}>
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<2x!quant.uniform<i8:f32, 0.1>>, tensor<2x!quant.uniform<i8:f32, 0.1>>) -> tensor<2x2x!quant.uniform<i8:f32, 0.1>>
  func.return %0 : tensor<2x2x!quant.uniform<i8:f32, 0.1>>
}

// -----