#include "tensorflow/core/platform/test.h"

using tensorflow::Status;
using tensorflow::string;
using tensorflow::TF_StatusPtr;

namespace tensorflow {
namespace {

// The tests are parameterized on:
// - a string representing the tracing implementation: "mlir" or "graphdef".
// - a boolean that when true enables TFRT as the execution engine.
class UnifiedCAPI

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/file_system_helper.h"
#include "tsl/platform/errors.h"

// TODO(b/139060984): After all filesystems are converted, all calls to
// methods from `FileSystem` will have to be replaced to calls to private
// methods here, as part of making this class a singleton and the only way to
// register/use filesystems.

# the dependencies of that target which include a license notice file are then
# included when generating that license. Necessary because the license targets
# in TensorFlow are manually enumerated rather than generated automatically.
do_external_licenses_check(){
  BUILD_TARGET="$1"
  LICENSES_TARGET="$2"

  # grep patterns for targets which are allowed to be missing from the licenses

    TFE_ContextOptions* options, const void* proto, size_t proto_len,
    TF_Status* status);

// Controls how to act when we try to run an operation on a given device but
// some input tensors are not on that device.
// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.

  sufficient description, if applicable unit tests are added, if it is a
  reasonable contribution (meaning it is not a single liner cosmetic PR).

**2. Valid?**

-   If the PR passes all the quality checks then we go ahead and assign a
    reviewer.
-   If the PR didn't meet the validation criteria, we request for additional
    changes to be made to PR to pass quality checks and send it back or on a
    rare occasion we may reject it.

**3. Review**

  // be an empty string if no device was explicitly requested, but will
  // otherwise be the name of this custom device. Ops are placed onto a custom
  // device if any of their inputs are on that custom device, but custom devices
  // are free to set a bad status in order to require explicit placement.
  void (*execute)(const TFE_Op* op, int* num_outputs,

      vspace, target_tensor_ids, source_tensor_ids, sources_that_are_targets,
      output_gradients, result, /*build_default_zeros_grads*/ false));
  return absl::OkStatus();
}

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.
// These APIs are mainly to facilitate testing and are subject to change.
namespace internal {

  switch (dtype.code) {
    case DLDataTypeCode::kDLBool:
      if (dtype.bits != 8) {
        return tensorflow::errors::InvalidArgument(
            "Only DLPack bools of bitwidth 8 are supported, got: ", dtype.bits);
      }
      *tf_dtype = TF_DataType::TF_BOOL;
      return absl::OkStatus();

    case DLDataTypeCode::kDLUInt:
      switch (dtype.bits) {
        case 8:

#define TP_OPTIMIZER_CONFIGS_STRUCT_SIZE \
  TF_OFFSET_OF_END(TP_OptimizerConfigs, scoped_allocator_optimization)

// Struct for Optimizer. Plugin authors must provide an optimize function.
// Creation and deletion functions are optional.
typedef struct TP_Optimizer {
  size_t struct_size;
  void* ext;  // reserved for future use

  // [Optional]
  // Create function for optimizer.
  void* (*create_func)();

  if (tp_configs.disable_model_pruning == TF_TriState_On)
    configs.disable_model_pruning = true;
  else
    configs.disable_model_pruning = false;
  // The other configs are turned on by default.
  CONFIG_TOGGLE(implementation_selector);
  CONFIG_TOGGLE(function_optimization);
  CONFIG_TOGGLE(common_subgraph_elimination);
  CONFIG_TOGGLE(arithmetic_optimization);