// tracing or immediate execution mode.
class AbstractOperation {
 protected:
  enum AbstractOperationKind {
    kGraph,
    kMlir,
    kEager,
    kTfrt,
    kTape,
    kOpHandler
  };
  explicit AbstractOperation(AbstractOperationKind kind) : kind_(kind) {}
  virtual ~AbstractOperation() {}

 public:
  AbstractOperationKind getKind() const { return kind_; }

        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",
        "@com_google_absl//absl/container:inlined_vector",
    ],
)

cc_library(
    name = "abstract_operation",
    hdrs = ["abstract_operation.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_tensor_handle",
        "//tensorflow/c:tensor_interface",

  virtual void SetStepId(int64_t step_id) = 0;

  // For LLVM style RTTI.
  static bool classof(const AbstractOperation* ptr) {
    return ptr->getKind() == kEager || ptr->getKind() == kTfrt;
  }

 protected:
  explicit ImmediateExecutionOperation(AbstractOperationKind kind)
      : AbstractOperation(kind) {}
  ~ImmediateExecutionOperation() override {}
};

namespace internal {

#define TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_

#include <memory>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {

// Abstract interface to a context.
//
// This serves as a factory for creating `AbstractOperation`s and for
// registering traced functions.
// Operations creation within a context can only be executed in that context
// (for now at least).

// It is allowed to reusing the same abstract operation for multiple execution
// on a given context, with the same or different input tensors.
class TracingOperation : public AbstractOperation {
 protected:
  explicit TracingOperation(AbstractOperationKind kind)
      : AbstractOperation(kind) {}

 public:
  // Sets the name of the operation: this is an optional identifier that is
  // not intended to carry semantics and preserved/propagated without

absl::Status SetAttrInt(AbstractOperation*, const char* attr_name,
                        int64_t value, ForwardOperation*);
absl::Status SetAttrFloat(AbstractOperation*, const char* attr_name,
                          float value, ForwardOperation*);
absl::Status SetAttrBool(AbstractOperation*, const char* attr_name, bool value,
                         ForwardOperation*);
absl::Status SetAttrType(AbstractOperation*, const char* attr_name,

}
absl::Status SetAttrFunction(AbstractOperation* op_, const char* attr_name,
                             const AbstractOperation* value,
                             ForwardOperation* forward_op_) {
  return tensorflow::errors::Unimplemented(
      "SetAttrFunction has not been implemented yet.");
}
absl::Status SetAttrFunctionName(AbstractOperation* op_, const char* attr_name,

  }
  absl::Status SetAttrFunctionList(
      const char* attr_name,
      absl::Span<const AbstractOperation*> values) override {
    return tensorflow::errors::Unimplemented(
        "SetAttrFunctionList has not been implemented yet.");
  }
  // For LLVM style RTTI.
  static bool classof(const AbstractOperation* ptr) {
    return ptr->getKind() == kGraph;
  }
  ~GraphOperation() override {}

 private:

                               const TFE_Op** value, int num_values) {
  auto s = tensorflow::unwrap(op)->SetAttrFunctionList(
      attr_name, {reinterpret_cast<const tensorflow::AbstractOperation**>(
                      tensorflow::unwrap(value)),
                  static_cast<size_t>(num_values)});
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}