#include <memory>

#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_operation.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/cancellation.h"
#include "tensorflow/core/framework/device_attributes.pb.h"
#include "tensorflow/core/framework/op_def.pb.h"

#ifndef TENSORFLOW_C_EAGER_TFE_TENSORHANDLE_INTERNAL_H_
#define TENSORFLOW_C_EAGER_TFE_TENSORHANDLE_INTERNAL_H_

#include "tensorflow/c/conversion_macros.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"

// Wraps a pointer to a tensor handle implementation.
//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying handle object. Instead, call

int TF_OutputListNumOutputs(TF_OutputList* o);
// Return the `i`th output in the list.
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i);
// Append a tensor at the end of the output list, growing its size by one.
void TF_OutputListPushBack(TF_OutputList* o, TF_AbstractTensor* tensor,
                           TF_Status*);

// TF_ExecuteOperation will, if in eager mode, execute, if in graph mode, maybe

#ifndef TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_
#define TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/unified_api_testutil.h"

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes
// `send_device`, `recv_device`, and `send_device_incarnation` set.
TFE_Op* SendOp(TFE_Context* ctx, TFE_TensorHandle* in,

// two component devices, running `x = TPUReplicatedInput(inputs=[a, b])` on the
// parallel device creates a parallel tensor `x` with `a` on the first of
// `underlying_devices` and `b` on the second. Running `a_unpacked, b_unpacked =
// TPUReplicatedOutput(input=x, num_replicas=2)` un-packs the parallel tensor
// into its components.
//
// The filled `device` struct and the allocated `device_info` struct may be

  // AssignSub, ...).
  void GeneralAssignment(const char* op_name, TFE_Context* context,
                         TFE_TensorHandle* value, TF_Status* status);

  // The a handle for the resource-dtype tensor pointing to the variable's
  // buffer.
  TFE_TensorHandle* handle_;
  // The dtype of the variable's buffer (input dtype for assignments, output
  // dtype of read operations).
  TF_DataType type_;
};

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_TENSOR_HANDLE_H_
#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_TENSOR_HANDLE_H_

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

#ifndef TENSORFLOW_C_EAGER_DLPACK_H_
#define TENSORFLOW_C_EAGER_DLPACK_H_

#include "tensorflow/c/eager/c_api.h"

namespace tensorflow {

// PyCapsule name for DLPack Tensor
const char* const kDlTensorCapsuleName = "dltensor";

// Converts eager tensor handle to DLPack (DLManagedTensor*), and return the
// void* for further PyCapsule construction.
TF_CAPI_EXPORT extern void* TFE_HandleToDLPack(TFE_TensorHandle* h,

#include "tensorflow/c/eager/tfe_monitoring_internal.h"  // IWYU pragma: export
#include "tensorflow/c/eager/tfe_op_attrs_internal.h"  // IWYU pragma: export
#include "tensorflow/c/eager/tfe_tensor_debug_info_internal.h"  // IWYU pragma: export

// TODO(b/154564140): Move this to its own header. This requires splitting
// c_api_experimental.h
struct TFE_ContextOptions {
  TF_SessionOptions session_options;