static void BoolDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<bool*>(data);
}

static void Int32Deallocator(void* data, size_t, void* arg) {
  delete[] static_cast<int32_t*>(data);
}

static void DoubleDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<double*>(data);
}

static void FloatDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<float*>(data);
}

  }
  const auto& s = attr->s();
  std::memcpy(value, s.data(), std::min<size_t>(s.length(), max_length));
}

void TF_OperationGetAttrStringList(TF_Operation* oper, const char* attr_name,
                                   void** values, size_t* lengths,
                                   int max_values, void* storage,
                                   size_t storage_size, TF_Status* status) {

    forward_op_->inputs.push_back(input);
  }
  return absl::OkStatus();
}

absl::Status SetAttrString(AbstractOperation* op_, const char* attr_name,
                           const char* data, size_t length,
                           ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, StringPiece(data, length));
  return op_->SetAttrString(attr_name, data, length);
}

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,

 public:
  virtual void Clear() = 0;

  // Returns the inputs of this op.
  virtual absl::Span<ImmediateExecutionTensorHandle* const> GetInputs()
      const = 0;
  virtual absl::Status SetInput(size_t index,
                                ImmediateExecutionTensorHandle* input) = 0;

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.

      const absl::optional<std::vector<int64_t>>& expected_shape) {
    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =
        TF_NewShapeAndTypeList(input_shapes_vec.size());
    for (size_t i = 0; i < input_shapes_vec.size(); ++i) {
      const auto& input_shape = input_shapes_vec[i];
      if (input_shape.has_value()) {
        TF_ShapeAndTypeListSetShape(input_shapes, i, input_shape->data(),

diff --git a/iconv/gconv.h b/iconv/gconv.h
index 3f9112e..8e60197 100644
--- a/iconv/gconv.h
+++ b/iconv/gconv.h
@@ -174,7 +174,7 @@ typedef struct __gconv_info
 {
   size_t __nsteps;
   struct __gconv_step *__steps;
-  __extension__ struct __gconv_step_data __data __flexarr;
+  __extension__ struct __gconv_step_data __data[0];
 } *__gconv_t;
 
 #endif /* gconv.h */
diff --git a/include/libc-symbols.h b/include/libc-symbols.h

		return
	}
	z.init = true
	C.mpz_init(&z.i[0])
}

// Bytes returns z's representation as a big-endian byte array.
func (z *Int) Bytes() []byte {
	b := make([]byte, (z.Len()+7)/8)
	n := C.size_t(len(b))
	C.mpz_export(unsafe.Pointer(&b[0]), &n, 1, 1, 1, 0, &z.i[0])
	return b[0:n]
}

// Len returns the length of z in bits.  0 is considered to have length 1.
func (z *Int) Len() int {
	z.doinit()

                              TF_Status* status) {
  status->status = MessageToBuffer(func->record->fdef(), output_func_def);
}

TF_Function* TF_FunctionImportFunctionDef(const void* proto, size_t proto_len,
                                          TF_Status* status) {
  tensorflow::FunctionDef fdef;
  bool success = fdef.ParseFromArray(proto, proto_len);
  if (!success) {
    status->status = InvalidArgument(

    return 1;
  }

  char file_name[100];
  time_t t = time(NULL);
  snprintf(file_name, sizeof(file_name), "test-%d-%ld.txt", getpid(), t);

  size_t length = 2 + strlen(path) + strlen(file_name);
  char* full_path = malloc(length);
  snprintf(full_path, length, "%s/%s", path, file_name);

  TF_WritableFileHandle* h;
  TF_Status* status = TF_NewStatus();