"//tensorflow/c/eager:c_api_experimental",
        "//tensorflow/c/eager:tfe_context_internal",
        "//tensorflow/core:framework",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/common_runtime/eager:context",
    ],
)

cc_library(
    name = "parallel_device_testlib",
    testonly = 1,

#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/graph/tensor_id.h"
#include "tensorflow/core/lib/core/status_test_util.h"

#include "tensorflow/c/experimental/gradients/tape/tape_context.h"
#include "tensorflow/c/experimental/ops/nn_ops.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {

using tensorflow::TF_StatusPtr;

Status ReluModel(AbstractContext* ctx,

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,
                                   absl::Span<AbstractTensorHandle* const>,
                                   absl::Span<AbstractTensorHandle*>)>;

// Runs `model` maybe wrapped in a function call op. This can be thought as
// being equivalent to the following python code.
//
// if use_function:

            "//tensorflow/core:core_cpu",
            "//tensorflow/core:core_cpu_internal",
            "//tensorflow/core:framework",
            "//tensorflow/core:lib",
            "//tensorflow/core:lib_internal",
            "//tensorflow/core:op_gen_lib",
            "//tensorflow/core:protos_all_cc",
            "//tensorflow/core/config:flag_defs",
            "//tensorflow/core/config:flags",

    ],
    deps = [
        ":modular_filesystem",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core/lib/io:path",
        "//tensorflow/core/platform:env",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:stacktrace_handler",
        "//tensorflow/core/platform:test",
    ],

#ifndef TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_
#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/refcount.h"
namespace tensorflow {
namespace tracing {
namespace graph {
using tensorflow::AbstractFunction;
// Thin wrapper around a FunctionDef.

            "//tensorflow/core:core_cpu",
            "//tensorflow/core:core_cpu_internal",
            "//tensorflow/core:framework",
            "//tensorflow/core:framework_internal",
            "//tensorflow/core:lib",
            "//tensorflow/core:lib_internal",
            "//tensorflow/core:protos_all_cc",
            "//tensorflow/core/common_runtime/eager:attr_builder",

    prove that your code works correctly, and b) guard against future breaking
    changes to lower the maintenance cost.
*   Bug fixes also generally require unit tests, because the presence of bugs
    usually indicates insufficient test coverage.
*   Keep API compatibility in mind when you change code in core TensorFlow,
    e.g., code in

        "//tensorflow/core/util:managed_stack_trace",
        "//tensorflow/core:all_kernels",
        "//tensorflow/core:core_cpu",
        "//tensorflow/core:direct_session",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core:graph",
        "//tensorflow/core:lib",
        "//tensorflow/core:lib_internal",
        "//tensorflow/core:ops",
        "//tensorflow/core:reader_base",