#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
class Device;
class DeviceMgr;
class ServerInterface;
}  // namespace tensorflow

// Internal structures used by the C API. These are likely to change and should
// not be depended on.

struct TF_SessionOptions {
  tensorflow::SessionOptions options;
};

struct TF_DeprecatedSession {
  tensorflow::Session* session;

///
/// All pointers defined in this structure point to memory allocated by the DSO
/// using an allocator provided by core TensorFlow when calling `TF_InitPlugin`.
///
/// IMPORTANT: To maintain binary compatibility, the layout of this structure
/// must not change! In the unlikely case that a new type of file needs to be
/// supported, add the new ops and metadata at the end of the structure.
typedef struct TF_FilesystemPluginOps {

// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,
// inputs and outputs and the gradient function These data structures combined
// allow us to trace the data dependencies between operations and hence compute
// gradients.
//
// `ZerosLike` is not expected to be called and returns a nullptr. The creation

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.
  vector<float> thetaPlus_data(num_elems);
  vector<float> thetaMinus_data(num_elems);
  AbstractTensorHandle* f_outputs[1];

  // Numerical Grad Check
  for (int i = 0; i < num_elems; i++) {

  TF_Operation* neg = TF_GraphOperationByName(graph, "imported/neg");
  ASSERT_TRUE(scalar != nullptr);
  ASSERT_TRUE(feed != nullptr);
  ASSERT_TRUE(neg != nullptr);

  // Test basic structure of the imported graph.
  EXPECT_EQ(0, TF_OperationNumInputs(scalar));
  EXPECT_EQ(0, TF_OperationNumInputs(feed));
  ASSERT_EQ(1, TF_OperationNumInputs(neg));
  TF_Output neg_input = TF_OperationInput({neg, 0});

#ifndef TENSORFLOW_C_EAGER_TAPE_H_
#define TENSORFLOW_C_EAGER_TAPE_H_

// Language-agnostic gradient tape. Does not perform backpropagation, just
// maintains the data structures required to do so.

#include <stack>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "tensorflow/core/config/flag_defs.h"
#include "tensorflow/core/config/flags.h"

      return tensorflow::Status(absl::StatusCode::kFailedPrecondition,    \
                                "Expected initialized `" #STRUCT_NAME     \
                                "` structure with `struct_size` field "   \
                                "set to " #SIZE_VALUE_NAME                \
                                ". Found `struct_size` = 0.");            \

 #ifndef _XLOCALE_H
 #define _XLOCALE_H	1
 
+#ifndef _BITS_TYPES___LOCALE_T_H
+#define _BITS_TYPES___LOCALE_T_H 1
+
 /* Structure for reentrant locale using functions.  This is an
    (almost) opaque type for the user level programs.  The file and
    this data structure is not standardized.  Don't rely on it.  It can
@@ -41,4 +44,6 @@ typedef struct __locale_struct
 /* POSIX 2008 makes locale_t official.  */

        TensorList operations.
    *   `tf.custom_gradient` can now be applied to functions that accept nested
        structures of `tensors` as inputs (instead of just a list of tensors).
        Note that Python structures such as tuples and lists now won't be
        treated as tensors, so if you still want them to be treated that way,
        you need to wrap them with `tf.convert_to_tensor`.

      CopyToCore<TF_ReadOnlyMemoryRegionOps>(
          info->ops[index].read_only_memory_region_ops,
          info->ops[index].read_only_memory_region_ops_size);

  // Step 2: Initialize the opaque filesystem structure
  auto filesystem = std::make_unique<TF_Filesystem>();
  TF_Status* c_status = TF_NewStatus();
  Status status = OkStatus();
  core_filesystem_ops->init(filesystem.get(), c_status);
  status = Status(c_status->status);