status->status = tensorflow::errors::InvalidArgument(
        "Got a null or uninitialized `op` argument");
    return;
  }
  tensorflow::EagerOperation* operation =
      OperationFromInterface(tensorflow::unwrap(const_cast<TFE_Op*>(op)));
  operation->MutableAttrs()->Set(attr_name, attr_value);
}

TF_CAPI_EXPORT extern int TFE_OpGetInputLength(TFE_Op* op,

variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a

#include "tensorflow/core/common_runtime/composite_device.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/eager/eager_operation.h"
#include "tensorflow/core/distributed_runtime/coordination/coordination_service_error_util.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/graph_debug_info.pb.h"

};

// Map from tensor_id to internally-defined operation-id of the operation which
// produced this tensor. A value of -1 means that the tensor was directly
// watched and not the result of any operation in the tape.
using TensorTape = std::unordered_map<int64_t, int64_t>;

// Map from operation-id to tape entry.
template <typename BackwardFunction, typename TapeTensor>
using OpTape =

          exempt-pr-labels: "override-stale"
          #Limit the No. of API calls in one run default value is 30.
          operations-per-run: 1000
          days-before-issue-stale: 7
          days-before-issue-close: 7
          stale-issue-label: "stale"
          # reason for closed the issue default value is not_planned

using tensorflow::errors::InvalidArgument;

namespace {
typedef std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)>
    UniqueFuncPtr;
}

// struct TF_Operation { tensorflow::Node node; };
static TF_Operation* ToTF_Operation(Node* node) {
  return static_cast<TF_Operation*>(static_cast<void*>(node));
}

void TF_EnableXLACompilation(TF_SessionOptions* options, unsigned char enable) {

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor...

#include "tensorflow/c/eager/c_api_remote_test_util.h"
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/core/common_runtime/eager/eager_operation.h"
#include "tensorflow/core/common_runtime/function_optimization_registry.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/framework/function.h"

                                        std::move(mixed_handles), status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK);
  // Can't take the shape of a parallel tensor with varying numbers of axes, but
  // running operations on them is OK.
  TF_ASSERT_OK(unknown_length_vector->Shape(&shape));
  EXPECT_THAT(*shape, ElementsAre(-1));
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> size_op(

#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/core/common_runtime/eager/eager_operation.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"