// Wraps a pointer to an operation implementation.
//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying operation object. Instead, call
// TFE_DeleteOp who calls Release() on the operation pointer and deletes
// the TFE_Op structure.
typedef struct TFE_Op TFE_Op;

namespace tensorflow {

DEFINE_CONVERSION_FUNCTIONS(tensorflow::ImmediateExecutionOperation, TFE_Op);

//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying handle object. Instead, call
// TFE_DeleteTensorHandle who calls Release() on the handle pointer and deletes
// the TFE_TensorHandle structure.
typedef struct TFE_TensorHandle TFE_TensorHandle;

namespace tensorflow {

DEFINE_CONVERSION_FUNCTIONS(tensorflow::ImmediateExecutionTensorHandle,

  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;
  tape_tensors.reserve(outputs.size());
  for (auto t : outputs) {
    tape_tensors.push_back(TapeTensor(t));
  }
  GradientTape::RecordOperation(
      op_name, tape_tensors, input_ids, input_dtypes,

  ~ImmediateExecutionOperation() override {}
};

namespace internal {
struct ImmediateExecutionOperationDeleter {
  void operator()(ImmediateExecutionOperation* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using ImmediateOpPtr =
    std::unique_ptr<ImmediateExecutionOperation,
                    internal::ImmediateExecutionOperationDeleter>;

   
   -----------------------------------------------------------------------------------------------
   
   **2. Installing **TensorFlow** (TF) CPU prebuilt binaries**
   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   

};

// GraphOperation wraps and populates a TF_OperationDescription.
class GraphOperation : public TracingOperation {
 public:
  explicit GraphOperation(TF_Graph* g) : TracingOperation(kGraph), g_(g) {}
  void Release() override { delete this; }
  Status Reset(const char* op, const char* raw_device_name) override {
    if (op_) {
      return errors::FailedPrecondition("Reset called on already built op.");
    }

  ::tensorflow::Status s =
      ::tensorflow::Env::Default()->NewWritableFile(filename, &f);
  ::tensorflow::Set_TF_Status_from_Status(status, s);

  if (s.ok()) {
    *handle = reinterpret_cast<TF_WritableFileHandle*>(f.release());
  }
}

void TF_CloseWritableFile(TF_WritableFileHandle* handle, TF_Status* status) {
  auto* cc_file = reinterpret_cast<::tensorflow::WritableFile*>(handle);
  TF_SetStatus(status, TF_OK, "");

#      Ex. no_docker  -- Disable docker on enabled platforms
#    See full examples below for more details on these. Some other modifiers are:
#      Ex. versions_upload -- for TF official release versions
#      Ex. nightly_upload -- for TF nightly official builds; changes version numbers
#      Ex. no_upload      -- Disable all uploads, usually for temporary CI issues

# Recommended: use a local+remote cache.
#

sed -i '54i#define TCP_USER_TIMEOUT 18' "/${TARGET}/usr/include/netinet/tcp.h"

# Download specific version of libstdc++ shared library based on the value of
# the `VERSION` parameter
  # Download binary libstdc++ 4.8 shared library release
wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 "http://old-releases.ubuntu.com/ubuntu/pool/main/g/gcc-4.8/libstdc++6_4.8.1-10ubuntu8_arm64.deb" && \
    unar "libstdc++6_4.8.1-10ubuntu8_arm64.deb" && \

  TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.

  Please upgrade your code to TensorFlow 2.0:
    * https://www.tensorflow.org/guide/migrate

  Or install the latest stable TensorFlow 1.X release:
    * `pip install -U "tensorflow==1.*"`

  Otherwise your code may be broken by the change.

  """)

# Make sure directory containing top level submodules is in