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_);

  for (int i = 0; i < noutputs; ++i) {
    output_names[i] = c_output_names[i];
  }
  std::vector<string> target_oper_names(ntargets);
  for (int i = 0; i < ntargets; ++i) {
    target_oper_names[i] = c_target_oper_names[i];
  }
  TF_Run_Helper(s->session, nullptr, run_options, input_pairs, output_names,
                c_outputs, target_oper_names, run_metadata, status);
}

void TF_PRunSetup(TF_DeprecatedSession* s,

    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",

  gsutil cp -n "$TFCI_OUTPUT_DIR"/*.whl "$TFCI_ARTIFACT_STAGING_GCS_URI"
fi

if [[ "$TFCI_WHL_BAZEL_TEST_ENABLE" == 1 ]]; then
  tfrun bazel test $TFCI_BAZEL_COMMON_ARGS --config="${TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX}_wheel_test"

        "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

  | sort -u
}

# Verify that, given a build target and a license-list generator target, all of
# the dependencies of that target which include a license notice file are then
# included when generating that license. Necessary because the license targets
# in TensorFlow are manually enumerated rather than generated automatically.
do_external_licenses_check(){
  BUILD_TARGET="$1"
  LICENSES_TARGET="$2"

  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())) {