// be a scalar typed field of the child structure (no nesting is supported).
	XListMapKeys []string

	// x-kubernetes-list-type annotates an array to further describe its topology.
	// This extension must only be used on lists and may have 3 possible values:
	//
	// 1) `atomic`: the list is treated as a single entity, like a scalar.

  }

  auto* ctx = reinterpret_cast<TF_OpKernelContext*>(cc_ctx);
  if (cc_a.dtype() == ::tensorflow::DT_VARIANT) {
    return VariantBinaryAddFunc(
        cc_ctx, cc_a.scalar<Variant>()(), cc_b.scalar<Variant>()(),
        cc_out->scalar<Variant>().data(), binary_add_func);
  } else {
    binary_add_func(ctx, a, b, out);
    return cc_ctx->status();
  }
}

static Status VariantBinaryAddFunc(

      }
    }

    // Special type fixes for scalar tensor types.
    // TFLite flatbuffer schema doesn't distinguish scalar tensor shapes
    // and unranked tensor shapes (i.e. they are both represented as an empty
    // INT32 list), see b/138865275. MLIR importer conservatively treats them as
    // unranked tensor types. Here we set them to scalar tensor types when it is
    // safe.

				{Name: string(v1.ResourceMemory), Weight: 1},
				{Name: "nvidia.com/gpu", Weight: 1},
			}},
			runPreScore: true,
		},
		// Only one node (node1) has the scalar resource, pod doesn't request the scalar resource and the scalar resource should be skipped for consideration.
		// Node1: std = 0, score = 100
		// Node2: std = 0, score = 100
		{
			pod:          st.MakePod().Obj(),

		panic("ed25519: bad seed length: " + strconv.Itoa(l))
	}

	h := sha512.Sum512(seed)
	s, err := edwards25519.NewScalar().SetBytesWithClamping(h[:32])
	if err != nil {
		panic("ed25519: internal error: setting scalar failed")
	}
	A := (&edwards25519.Point{}).ScalarBaseMult(s)

	publicKey := A.Bytes()

	copy(privateKey, seed)
	copy(privateKey[32:], publicKey)
}

		},
		"removed only": {
			Original: `{"name":"foo","scalar":true,"complex":{"nested":true}}`,
			Current:  `{"name":"foo","scalar":true,"complex":{"nested":true},"array":[1,2,3]}`,
			Modified: `{"name":"foo"}`,

			ExpectedTwoWay:         `{"complex":null,"scalar":null}`,
			ExpectedTwoWayResult:   `{"name":"foo"}`,
			ExpectedThreeWay:       `{"complex":null,"scalar":null}`,

                                  ShapedType result_ty) -> bool {
    // Scalar identity is broadcastable to any operand shape, we only need to
    // check that operand has the same shape as a result.
    bool scalar_identity = identity_ty.hasRank() && identity_ty.getRank() == 0;
    if (scalar_identity) return operand_ty == result_ty;

    // If identity is not a scalar, we must verify that identity shape is

  /// construct an Initializer. Initializer stores the value it got constructed
  /// with in a Tensor object.
  struct Initializer {
    /// Construct from a scalar value of an arithmetic type or a type that can
    /// be converted to a string (eg. a string literal).
    template <typename T, typename = typename std::enable_if<
                              std::is_arithmetic<T>::value ||

  TFE_OpSetAttrType(fill_op, "T", TF_FLOAT);
  TFE_OpSetAttrType(fill_op, "Tshape", TF_INT32);

  float five = 5.0;
  TFE_TensorHandle* scalar = TestScalarTensorHandle(tfe_context_, five);
  TF_Tensor* scalarTensor = TFE_TensorHandleResolve(scalar, status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  CheckOutputShapes(fill_op,

// bitstream.
func (w *Encoder) Value(val constant.Value) {
	w.Sync(SyncValue)
	if w.Bool(val.Kind() == constant.Complex) {
		w.scalar(constant.Real(val))
		w.scalar(constant.Imag(val))
	} else {
		w.scalar(val)
	}
}

func (w *Encoder) scalar(val constant.Value) {
	switch v := constant.Val(val).(type) {
	default:
		errorf("unhandled %v (%v)", val, val.Kind())
	case bool:
		w.Code(ValBool)