virtual AbstractTensorInterface* CreateBoolScalar(bool value) = 0;

  // Tensor creation functions
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, absl::Span<const int64_t> dim_sizes) = 0;

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

  // Create a tensor instance from the given data buffer and description.

			parsedField, ok = s.FieldsByName[f.Name]
		}

		if !ok {
			t.Errorf("schema %v failed to look up field with name %v", s, f.Name)
		} else {
			tests.AssertObjEqual(t, parsedField, f, "Name", "DBName", "BindNames", "DataType", "PrimaryKey", "AutoIncrement", "Creatable", "Updatable", "Readable", "HasDefaultValue", "DefaultValue", "NotNull", "Unique", "Comment", "Size", "Precision", "TagSettings")

			if f.DBName != "" {

                                          const char* attr_name,
                                          TF_DataType value);
TF_CAPI_EXPORT extern void TF_SetAttrTypeList(TF_OperationDescription* desc,
                                              const char* attr_name,
                                              const TF_DataType* values,
                                              int num_values);

    return nullptr;
  }
  TFE_TensorHandleGetStatus(components[0].get(), status);
  if (!status->status.ok()) {
    return nullptr;
  }

  TF_DataType dtype = TFE_TensorHandleDataType(components[0].get());
  // Verify that the TensorHandle's shape and dtype match all of the component
  // shapes and dtypes.
  for (TensorHandlePtr& component : components) {

                                           const int64_t** shapes,
                                           const int* ranks,
                                           const TF_DataType* types,
                                           TF_Status* status);

void RecordMutation(TF_Graph* graph, const TF_Operation& op,
                    const char* mutation_type)

//
// Custom devices must use TFE_NewCustomDeviceTensorHandle instead.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(
    TFE_Context* ctx, const char* device_name, TF_DataType, const int64_t* dims,
    int num_dims, void* data, size_t len,
    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status);

  for (auto input : inputs) {
    PartialTensorShape shape;
    TF_RETURN_IF_ERROR(input->Shape(&shape));
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
        input->DataType(), shape, &handle));
    params->emplace_back(handle);
  }
  return absl::OkStatus();
}

// Runs `model` maybe wrapped in a function.
Status RunModel(Model model, AbstractContext* ctx,

								if field.DBName != "" {
									conds = append(conds, clause.Eq{Column: clause.Column{Table: clause.CurrentTable, Name: field.DBName}, Value: v})
								} else if field.DataType != "" {
									conds = append(conds, clause.Eq{Column: clause.Column{Table: clause.CurrentTable, Name: field.Name}, Value: v})
								}
							}
						}
					}
				case reflect.Slice, reflect.Array:

class TapeTensor {
 public:
  explicit TapeTensor(AbstractTensorHandle* handle);
  TapeTensor(const TapeTensor& other);
  ~TapeTensor();

  int64_t GetID() const;
  tensorflow::DataType GetDType() const;

  AbstractTensorHandle* ZerosLike() const;

  AbstractTensorHandle* GetHandle() const;

 private:
  AbstractTensorHandle* handle_;
};

// correspond fairly well to the implementation, but testing the C++ directly is
// another option.

namespace tensorflow {
namespace parallel_device {

Variable* Variable::Create(TFE_Context* context, TF_DataType type,
                           const int64_t* dims, const int num_dims,
                           const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(