}

  // 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.

    MATCH(DT_BFLOAT16, half);
    MATCH(DT_STRING, string);

    // TODO(b/188995810): DenseElementsAttr::get doesn't support complex
    // Attributes being passed, so we bail out for now. This should just be
    //   MATCH(DT_COMPLEX64, scomplex) / 2;
    //   MATCH(DT_COMPLEX128, dcomplex) / 2;
    // when DenseElementsAttr is updated.
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    default:
      return -1;
  }

     * against the property value for later invocations of the task, to determine if the task is up-to-date.</p>
     *
     * <p>The given value must be a simple value, like a String or Integer, or serializable. For complex values,
     *  Gradle compares the serialized forms for detecting changes and the {@code equals()} method is ignored.
     *

        then:
        analysis.accessibleClassDependencies == [SomeClass.name] as Set
        analysis.privateClassDependencies == [YetAnotherClass.name] as Set
    }

    def "knows dependencies of a java class complex"() {
        when:
        def analysis = analyze(SomeClass)

        then:
        analysis.accessibleClassDependencies == [AccessedFromPackagePrivateField.name] as Set

    FastAPI will use the `response_model` parameter (that includes dataclasses) to convert the response.

You can combine `dataclasses` with other type annotations in many different combinations to form complex data structures.

Check the in-code annotation tips above to see more specific details.

## Learn More

keyword:/\b(?:break|case|chan|const|continue|default|defer|else|fallthrough|for|func|go(?:to)?|if|import|interface|map|package|range|return|select|struct|switch|type|var)\b/,builtin:/\b(?:bool|byte|complex(?:64|128)|error|float(?:32|64)|rune|string|u?int(?:8|16|32|64)?|uintptr|append|cap|close|complex|copy|delete|imag|len|make|new|panic|print(?:ln)?|real|recover)\b/,boolean:/\b(?:_|iota|nil|true|false)\b/,operator:/[*\/%^!=]=?|\+[=+]?|-[=-]?|\|[=|]?|&(?:=|&|\^=?)?|>(?:>=?|=)?|<(?:<=?|=|-)?|:=|\....