// restricted stand-in for tf.Variable in Python.
class Variable {
 public:
  // Construct a Variable from a resource-dtype TFE_TensorHandle and an
  // indication of the dtype of the variable's value.
  //
  // Note that creating this resource-dtype handle can fail, so `Create` is a
  // separate static method which returns a status.
  Variable(TFE_TensorHandle* handle, TF_DataType type)
      : handle_(handle), type_(type) {}

                                                 TF_Output* inputs,
                                                 int max_inputs);

// Get the number of current consumers of a specific output of an
// operation.  Note that this number can change when new operations
// are added to the graph.
TF_CAPI_EXPORT extern int TF_OperationOutputNumConsumers(TF_Output oper_out);

// Get list of all current consumers of a specific output of an

    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.
  TFE_DEVICE_PLACEMENT_EXPLICIT = 0,

typedef jarray jshortArray;
typedef jarray jintArray;
typedef jarray jlongArray;
typedef jarray jfloatArray;
typedef jarray jdoubleArray;
typedef jarray jobjectArray;

typedef jobject jweak;

// policy is applied to any subsequent op executions.
TF_CAPI_EXPORT void TFE_ContextSetSoftDevicePlacement(TFE_Context* ctx,
                                                      unsigned char enable,
                                                      TF_Status* status);

// Configure device placement policy logging for the eager executor. Note this

  void LookupOrCompute(const std::string& key, T* value,
                       const ComputeFunc& compute_func, TF_Status* status) {
    if (max_age_ == 0) {
      return compute_func(key, value, status);
    }

    // Note: we hold onto mu_ for the rest of this function. In practice, this
    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per

    TF_ShapeAndTypeList** shape_list_array, int num_items);

// Infer shapes for the given `op`. The arguments mimic the arguments of the
// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`

    ids.push_back(i);
  }
  const int64_t op_id = next_op_id_++;
  std::vector<TapeTensor> tensors;
  tensors.reserve(output_tensors.size());
  for (const TapeTensor& o : output_tensors) {
    // Note: the tensor can have already been watched and hence be in the tape,
    // so we cannot check that we're inserting it here.
    tensor_tape_[o.GetID()] = op_id;
    tensor_usage_[o.GetID()] = 1;
    tensors.push_back(o);

//       `struct_size` as well, along with all other fields.
//     * Refer to "TensorFlow Versioning Strategy" section at
//       https://github.com/tensorflow/community/pull/257/files.
//     * Note that the API is still under active development and doesn't have
//       versioning guarantees yet.
//   * `void* ext` is a free-form field that can be populated by

  // Returns tensor (full) type.
  // While there is no immediate plan to deprecate dtype and shape in favor
  // of only using full type type information, this is a future possibility.
  //
  // Note that map_dtype_to_child_of_tensor() from core/framework/types.h
  // can be used to set a FullTypeDef based on dtype in a derived class if
  // appropriate.
  virtual tensorflow::FullTypeDef FullType() const = 0;