let arguments = (ins
    TFL_TensorOf<[Complex<F<32>>, Complex<F<64>>]>:$input
  );

  let results = (outs
    TFL_TensorOf<[F32, F64]>:$output
  );
}

def TFL_RealOp : TFL_Op<"real", [
  Pure,
  SameOperandsAndResultShape]> {
  let summary = "Returns the real part of a complex number.";

  let description = [{
Given a tensor `input` of complex numbers, this operation returns a tensor of

import org.gradle.api.Incubating;

import java.util.Collection;

/**
 * Provides infrastructure to select which test classes, methods, and packages will be included in the test execution.
 * <p>
 * A complex example:
 * <pre>
 * TestLauncher testLauncher = projectConnection.newTestLauncher();
 * testLauncher.withTestsFor(specs -&gt; {

		panic("unreachable")
	}
	a := s.Sizeof(T) // may be 0 or negative
	// spec: "For a variable x of any type: unsafe.Alignof(x) is at least 1."
	if a < 1 {
		return 1
	}
	// complex{64,128} are aligned like [2]float{32,64}.
	if isComplex(T) {
		a /= 2
	}
	if a > s.MaxAlign {
		return s.MaxAlign
	}
	return a
}

func _IsSyncAtomicAlign64(T Type) bool {
	named := asNamed(T)
	if named == nil {

	if r := recover(); r != nil {
		if s, ok := r.(string); ok {
			if strings.HasPrefix(s, panicPrefix) {
				return
			}
		}
		panic(r)
	}
}

// New creates a new fuzz.Helper, capable of generating more complex types
func New(t test.Failer, data []byte) Helper {
	return Helper{cf: fuzzheaders.NewConsumer(data), t: t}
}

  }

  // CHECK-LABEL: mirror_pad
  func.func @mirror_pad(%arg0: tensor<2x3xcomplex<f64>>) -> tensor<4x7xcomplex<f64>> {
    %0 = mhlo.constant dense<[[1, 1], [2, 2]]> : tensor<2x2xi32>
    // CHECK-NOT: tf.MirrorPad
    %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>
    func.return %1 : tensor<4x7xcomplex<f64>>
  }

  auto re = Real(scope, op.input(0));
  auto im = Imag(scope, op.input(0));
  auto z_inv = Reciprocal(scope, Complex(scope, im, re));
  auto zero = Cast(scope, Const(scope, 0), grad_inputs[0].type());
  auto grad = Complex(scope, grad_inputs[0], zero);
  auto dx = Neg(scope, Mul(scope, grad, z_inv));
  grad_outputs->push_back(dx);
  return scope.status();
}
REGISTER_GRADIENT_OP("Angle", AngleGrad);

			// impossible or has failed", using particular placeholder functions
			// to represent common failure cases. Look for those functions in
			// the leaf position as a sign that the call stack and its
			// symbolization are more complex than this test can handle.
			//
			// TODO: Make the symbolization done by the execution tracer and CPU
			// profiler match up even in these harder cases. See #53378.
			switch leaf {

*Binary Plugins* are full-fledged plugins written in Java, Groovy, or Kotlin, compiled into JAR files, and published to a repository.
They offer the best performance, maintainability, and reusability.
They are suitable for complex build logic that needs to be shared across projects, builds, and teams.
You can also write them in Scala or Groovy but that is not recommended.

Here is a breakdown of all options for implementing Gradle plugins:

  Status AllocateShapedBuffer(DataType dtype, const xla::Shape& on_device_shape,
                              xla::LocalClient* client, int device_ordinal);

  // Some Tensors can have complex on-device shapes, including tuple shapes. To
  // manage the memory for these tensors a ShapedBuffer may be required.

  // Return true if this XlaTensor contains a ShapedBuffer.

pkg encoding/asn1, const TagUTF8String ideal-int
pkg go/build, const IgnoreVendor = 8
pkg go/build, const IgnoreVendor ImportMode
pkg go/build, type Package struct, InvalidGoFiles []string
pkg go/constant, func ToComplex(Value) Value
pkg go/constant, func ToFloat(Value) Value
pkg go/constant, func ToInt(Value) Value
pkg go/constant, type Value interface, ExactString() string
pkg go/types, method (*Package) SetName(string)