//
// auto* reader = TFE_MonitoringNewCounterReader("name");
// test();
// int64_t value = TFE_MonitoringReadCounter1(reader, "label-value");

// Opaque handle to a reader.
typedef struct TFE_MonitoringCounterReader TFE_MonitoringCounterReader;

// Returns a handle to be used for reading values from streamz counter. The
// counter can have been created with any number of labels.

  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {
    return ptr->getKind() == kGraph || ptr->getKind() == kMlir;
  }
};

typedef TracingContext* (*FactoryFunction)(const char* fn_name, TF_Status*);
absl::Status SetDefaultTracingEngine(const char* name);
void RegisterTracingEngineFactory(const ::tensorflow::string& name,

#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/types.h"

using tensorflow::string;

namespace tensorflow {
namespace tracing {
typedef absl::flat_hash_map<std::string, tracing::FactoryFunction> FactoriesMap;

static FactoriesMap& GetFactories() {
  static FactoriesMap* factories = new FactoriesMap;
  return *factories;
}

  virtual AbstractTensorInterface* CreateBoolScalar(bool value) = 0;

  // Tensor creation functions
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, absl::Span<const int64_t> dim_sizes) = 0;

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for

using tensorflow::FunctionDef;
using tensorflow::Node;
using tensorflow::NodeBuilder;
using tensorflow::Status;
using tensorflow::errors::InvalidArgument;

namespace {
typedef std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)>
    UniqueFuncPtr;
}

// struct TF_Operation { tensorflow::Node node; };
static TF_Operation* ToTF_Operation(Node* node) {

namespace tensorflow {
namespace {

// Specification for expected input/output and its type.
// DataType value of DT_INVALID signifies that we don't want to
// check the data type.
typedef std::pair<std::string, DataType> IOSpec;

const char* kFeedStackToString = "File \"feed.cc\", line 10, in alpha";
const char* kNegStackToString = "File \"neg.cc\", line 15, in beta";

declare Go variables which hold C function pointers and pass them
back and forth between Go and C. C code may call function pointers
received from Go. For example:

	package main

	// typedef int (*intFunc) ();
	//
	// int
	// bridge_int_func(intFunc f)
	// {
	//		return f();
	// }
	//
	// int fortytwo()
	// {
	//	    return 42;
	// }
	import "C"
	import "fmt"

  {
    TF_Buffer* op_list_buffer = TF_GetAllOpList();
    tensorflow::OpList op_list;
    op_list.ParseFromArray(op_list_buffer->data, op_list_buffer->length);
    ASSERT_GE(op_list.op_size(), 1);
    typedef tensorflow::protobuf::RepeatedPtrField<tensorflow::OpDef> OpDefs;
    const OpDefs& ops = op_list.op();
    bool found = std::find_if(ops.begin(), ops.end(),
                              [](const tensorflow::OpDef& op_def) {

	<br>
	<code>typeof(v) ≡<sub>A</sub> typeof(f)</code>.
	</p>
</li>
<li>
	<p>
	For a return statement <code>return …, f, … </code> where <code>f</code> is a
	generic function returned as a result to a (non-generic) result variable
	<code>r</code> of function type:
	<br>
	<code>typeof(r) ≡<sub>A</sub> typeof(f)</code>.
	</p>
</li>
</ul>

<p>

 * interfaces or extended classes.
 * <p>
 * Example usage:
 * <pre>
 * {@literal @}Typed(ServiceImpl.class)
 * public class ServiceImpl implements Service {
 *     // Implementation
 * }
 * </pre>
 *
 * @since 4.0.0
 */
@Target({FIELD, METHOD, TYPE})
@Retention(RUNTIME)
@Documented
public @interface Typed {
    /**
     * Specifies the types that should be considered for dependency injection.
     * <p>