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

namespace tensorflow {

// Abstract interface to a TensorHandle.
//
// A TensorHandle is management class around a Tensor which may track additional
// metadata and synchronization.
//
// This allows us to hide concrete implementations of TensorHandle from header
// files. The interface lists the common functionality that must be provided by

  TF_Buffer* run_metadata = TF_NewBuffer();
  TF_Run(session, run_options, nullptr, nullptr, 0, nullptr, nullptr, 0,
         nullptr, 0, run_metadata, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ("Session was not created with a graph before Run()!",
            string(TF_Message(s)));
  TF_DeleteBuffer(run_metadata);
  TF_DeleteBuffer(run_options);

            return metadata;
          }
        }
      }
      break;

    case tensorflow::AttrValue::kPlaceholder:
      metadata.is_list = 0;
      metadata.list_size = -1;
      metadata.type = TF_ATTR_PLACEHOLDER;
      metadata.total_size = -1;
      break;

    case tensorflow::AttrValue::kFunc:
      metadata.is_list = 0;
      metadata.list_size = -1;

TF_CAPI_EXPORT extern void TFE_ContextDisableRunMetadata(TFE_Context* ctx);

// Populates the passed-in buffer with a serialized RunMetadata protocol buffer
// containing any run metadata information accumulated so far and clears this
// information.
// If async mode is enabled, this call blocks till all currently pending ops are
// done.

// type of eager and graph tensors. It is also the result of executing an
// operation.
typedef struct TF_AbstractTensor TF_AbstractTensor;

// A TF_AbstractOp is the metadata we need to execute an operation. E.g. this
// could contain the op type and other attributes.
typedef struct TF_AbstractOp TF_AbstractOp;

// Stores a function representation that can be used for execution or for

  auto metadata = gcs_client->GetObjectMetadata(
      bucket, object, gcs::Fields("generation,size,timeStorageClassUpdated"));
  if (!metadata) return TF_SetStatusFromGCSStatus(metadata.status(), status);
  stat->generation_number = metadata->generation();
  stat->base.length = metadata->size();
  stat->base.mtime_nsec =
      metadata->time_storage_class_updated().time_since_epoch().count();

/// tied with TensorFlow releases and should have their own versioning metadata
/// in addition with the data discussed in this section. Each plugin author can
/// use a custom scheme, but it should only relate to changes in plugin code.
/// This section only touches metadata related to the versioning of this
/// interface that is shared by all possible plugins.
///

                         absl::Span<AbstractTensorHandle* const> grad_outputs,
                         absl::Span<AbstractTensorHandle*> grad_inputs) = 0;
  virtual ~GradientFunction() {}
};

// Metadata from the forward operation that is made available to the
// gradient registerer to instantiate a GradientFunction.
struct ForwardOperation {
 public:
  string op_name;
  std::vector<AbstractTensorHandle*> inputs;

  auto* context = tensorflow::unwrap(ctx);
  status->status = context->AsyncWait();
  if (!status->status.ok()) return;
  auto run_metadata = context->ExportRunMetadata();
  status->status = MessageToBuffer(*run_metadata, buf);
}

namespace {
TFE_Op* GetFunc(TFE_Context* ctx, const tensorflow::NameAttrList& func,
                TF_Status* status) {

### Known Caveats

* `tf.lite`
    * when converter flag "_experimenal_use_buffer_offset" is enabled, additional metadata is automatically excluded from the generated model. The behaviour is the same as "exclude_conversion_metadata" is set
    * If the model is larger than 2GB, then we also require "exclude_conversion_metadata" flag to be set

### Major Features and Improvements