# TODO(gunan): Add sanity checks to loaded modules here.

  # Load first party dynamic kernels.
  _tf_dir = _os.path.dirname(_current_file_location)
  _kernel_dir = _os.path.join(_tf_dir, "core", "kernels")
  if _os.path.exists(_kernel_dir):
    _ll.load_library(_kernel_dir)

  # Load third party dynamic kernels.
  for _s in _site_packages_dirs:
    _plugin_dir = _os.path.join(_s, "tensorflow-plugins")

  echo "Use 'devtoolset-9' to build a manylinux2014 compatible toolchain"
  exit 1
  ;;
esac

mkdir -p "${TARGET}"

mkdir -p ${TARGET}/usr/include

# Put the current kernel headers from ubuntu in place.
ln -s "/usr/include/linux" "${TARGET}/usr/include/linux"
ln -s "/usr/include/asm-generic" "${TARGET}/usr/include/asm-generic"
ln -s "/usr/include/aarch64-linux-gnu/asm" "${TARGET}/usr/include/asm"

        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

tf_custom_op_library(
    name = "test_op1.so",
    srcs = ["test_op1.cc"],
)

tf_kernel_library(
    name = "test_op_kernel",
    srcs = ["test_op.cc"],
    deps = [
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
    ],
    alwayslink = 1,
)

tf_cuda_cc_test(

  // Get a list of the names of functions that have been registered.
  virtual std::vector<string> ListFunctionNames() = 0;

  struct CacheStats {
    int64_t kernel_cache_size;
    int64_t device_cache_size;
    std::map<std::string, int64_t> func_kernel_cache_entries;
    int64_t local_rendezvous_cache_active_size;
  };
  virtual CacheStats GetCacheStats() = 0;

    ```bash
    bazel test ${flags} //tensorflow/python/...
    ```

    For a single component e.g. softmax op:

    ```bash
    bazel test ${flags} tensorflow/python/kernel_tests/nn_ops:softmax_op_test
    ```

    For a single/parameterized test e.g. `test_capture_variables` in
    `tensorflow/python/saved_model/load_test.py`:

    (Requires `python>=3.7`)

    ```bash

TF_CAPI_EXPORT extern bool TFE_ExecutorIsAsync(TFE_Executor*);

// Causes the calling thread to block till all ops dispatched in this executor
// have been executed. Note that "execution" here refers to kernel execution /
// scheduling of copies, etc. Similar to sync execution, it doesn't guarantee
// that lower level device queues (like GPU streams) have been flushed.
//

#     visibility = ["//visibility:public"],
# )
# copybara:uncomment_end

# 'enable_registration_v2' opts-in to a different implementation of op and
# kernel registration - REGISTER_OP, REGISTER_KERNEL_BUILDER, etc.
#
# This setting is currently experimental. The 'v2' implementation does _not_
# correspond to a particular, finalized design; rather, it relates to
# developing one.
#

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

  ASSERT_EQ(kernel_list.kernel_size(), 1);
  EXPECT_EQ(kernel_list.kernel(0).op(), "TestOpWithSingleKernel");
  EXPECT_EQ(kernel_list.kernel(0).device_type(), "CPU");
  TF_DeleteBuffer(kernel_list_buf);
  TF_DeleteStatus(status);
}

TEST(TestKernel, TestGetRegisteredKernelsForOpNoKernels) {
  TF_Status* status = TF_NewStatus();

            "//tensorflow/core/common_runtime/eager:eager_operation",
            "//tensorflow/core/common_runtime/eager:execute",
            "//tensorflow/core/common_runtime/eager:kernel_and_device",
            "//tensorflow/core/common_runtime/eager:tensor_handle",
            "//tensorflow/core/lib/llvm_rtti",
            "@com_google_absl//absl/types:variant",
        ],
    }) + select({