TestEncodeDecode(__LINE__, {"hello"});
  TestEncodeDecode(__LINE__,
                   {"the", "quick", "brown", "fox", "jumped", "over"});

  string big(1000, 'a');
  TestEncodeDecode(__LINE__, {"small", big, "small2"});
}

TEST(CAPI, SessionOptions) {
  TF_SessionOptions* opt = TF_NewSessionOptions();
  TF_DeleteSessionOptions(opt);
}

TEST(CAPI, DeprecatedSession) {
  TF_Status* s = TF_NewStatus();

        "//tensorflow/core/platform:status",
        "@local_tsl//tsl/platform:errors",
    ],
)

# Compliance test for modules and for interface
tf_cc_test(
    name = "modular_filesystem_test",
    size = "small",
    srcs = ["modular_filesystem_test.cc"],
    linkopts = ["-ldl"],
    tags = [
        "manual",  # Requires DSOs as arguments, eventual setup

navigate to the
[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.
[issues with the "good first issue" label](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue)
and then take a look at the

  // to pin to the physical device.
  //
  // This function is guaranteed to be called only when all of the custom-device
  // inputs are on this device.
  bool (*shall_pin_to_this_device)(const TFE_Op* op, TF_Status* s) = nullptr;
} TFE_CustomDevice;

// Registers a custom device for use with eager execution.
//
// Eager operations may be placed on this device, e.g.  `with

        "//tensorflow/core/platform:status",
        "@com_google_absl//absl/container:flat_hash_set",
    ],
)

tf_cuda_cc_test(
    name = "gradients_test",
    size = "small",
    srcs = [
        "gradients_test.cc",
    ],
    args = ["--heap_check="],
    tags = tf_cuda_tests_tags() + ["nomac"],
    deps = [
        ":abstract_context",
        ":abstract_tensor_handle",

    TF_Status status;
    // Let this custom device choose the device to pin this op on if it
    // implements the pinning function.
    if (device_.shall_pin_to_this_device != nullptr) {
      return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status);
    }
    return errors::Unimplemented("No custom device pinning implementation.");
  }

 private:
  TFE_Context* context_;

*   `tf.math`

    * `tf.nn.top_k` now supports specifying the output index type via parameter `index_type`.  Supported types are `tf.int16`, `tf.int32` (default), and `tf.int64`.

        ":not_differentiable",
        "//tensorflow/c/eager:abstract_context",
        "//tensorflow/c/eager:gradients_internal",
    ],
)

tf_cuda_cc_test(
    name = "custom_gradient_test",
    size = "small",
    srcs = [
        "custom_gradient_test.cc",
    ],
    args = ["--heap_check="],  # TODO(b/174752220): Remove
    tags = tf_cuda_tests_tags(),
    deps = [
        "//tensorflow/c:tf_status_helper",

        "//tensorflow/c:tf_status",
        "@com_google_absl//absl/base:core_headers",
        "@com_google_absl//absl/synchronization",
    ],
)

tf_cc_test(
    name = "ram_file_block_cache_test",
    size = "small",
    srcs = ["ram_file_block_cache_test.cc"],
    deps = [
        ":ram_file_block_cache",
        "//tensorflow/c:tf_status_internal",
        "//tensorflow/core:lib",
        "//tensorflow/core:test",

build:windows --cxxopt=/std:c++17
build:windows --host_cxxopt=/std:c++17

# On windows, we still link everything into a single DLL.
build:windows --config=monolithic

# On linux, we dynamically link small amount of kernels
build:linux --config=dynamic_kernels

# Make sure to include as little of windows.h as possible
build:windows --copt=-DWIN32_LEAN_AND_MEAN
build:windows --host_copt=-DWIN32_LEAN_AND_MEAN