TF_Output fetches[] = {dxy_dx, dxy_dy};
  TF_Tensor* fetchValues[] = {nullptr, nullptr};

  TF_SessionRun(sess, nullptr /* run_options */, feeds, feedValues, 2, fetches,
                fetchValues, 2, nullptr /* target_opers */, 0,
                nullptr /* run_metadata */, s_);
  TF_DeleteTensor(feedValues[0]);
  TF_DeleteTensor(feedValues[1]);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  TF_DeleteSession(sess, s_);

  TF_Graph* parent = params->cond_graph->parent;
  TF_Output* parent_inputs = params->cond_graph->parent_inputs;
  int num_loop_vars = params->ninputs;

  mutex_lock l(parent->mu);

  // 'cond_fn' copies the cond graph into the parent graph.
  tensorflow::ops::CondGraphBuilderFn cond_fn =
      [params, parent](const tensorflow::Scope& scope,

    hdrs = [":includes"],
    strip_include_prefix="site-packages/numpy/core/include/",
)
""",
    ),
}

pip_parse(
    name = "pypi",
    annotations = NUMPY_ANNOTATIONS,
    python_interpreter_target = interpreter,
    requirements = "//:requirements_lock_" + TF_PYTHON_VERSION.replace(".", "_") + ".txt",
)

load("@pypi//:requirements.bzl", "install_deps")

install_deps()

        "//tensorflow/core:testlib",
        "@com_google_absl//absl/strings",
    ],
)

# -----------------------------------------------------------------------------
# Python API target

tf_cuda_library(
    name = "python_api",
    srcs = ["python_api.cc"],
    hdrs = ["python_api.h"],
    visibility = ["//tensorflow/python:__pkg__"],
    deps = [
        ":c_api",

      tensorflow::unwrap(ctx)->GetDistributedManager();
  tsl::CoordinationServiceAgent* coord_agent =
      dist_mgr->GetCoordinationServiceAgent();
  if (coord_agent == nullptr) {
    status->status = tensorflow::errors::FailedPrecondition(
        "Coordination service agent is not enabled.");
    return;
  }
  status->status = coord_agent->InsertKeyValue(key, value);
}

        "cpu": "mips64",
    },
    visibility = ["//visibility:public"],
)

# TODO(jakeharmon8): Remove in favor of TSL version
config_setting(
    name = "windows",
    # Internal builds query the target OS.
    constraint_values = if_google(
        ["//third_party/bazel_platforms/os:windows"],
        [],
    ),
    # OSS builds query the CPU type.
    values = if_oss(
        {"cpu": "x64_windows"},

      ImmediateExecutionTensorHandle* handle,
      const tensorflow::string& target_device_name,
      ImmediateExecutionTensorHandle** result) override {
    TF_Status status;
    handle->Ref();
    TFE_TensorHandle* result_handle = device_.copy_tensor_from_device(
        context_, tensorflow::wrap(handle), target_device_name.c_str(), &status,
        info_);
    handle->Unref();

*   <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER>

*   Add `is_cpu_target_available`, which indicates whether or not TensorFlow was
    built with support for a given CPU target. This can be useful for skipping
    target-specific tests if a target is not supported.

*   `tf.data`
    * Support `data.experimental.distribued_save`. `distribued_save` uses
      tf.data service

        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/time",
        "@local_xla//xla/tsl/c:tsl_status_internal",
        "@local_xla//xla/tsl/distributed_runtime/coordination:coordination_service_agent",
    ],
    alwayslink = 1,
)

cc_library(
    name = "c_api_experimental_reader",
    testonly = True,
    srcs = [
        "c_api_experimental_reader.cc",
    ],
    hdrs = [

  in_grads.reserve(input_tensors.size());
  for (int target_index = 0; target_index < input_tensors.size();
       ++target_index) {
    const auto current_grad =
        accumulated_gradients_.find(input_tensors[target_index].GetID());
    if (current_grad == accumulated_gradients_.end()) {
      if (IsDtypeTrainable(input_tensors[target_index].GetDType())) {