"//tensorflow/compiler/mlir/tensorflow",
        "//tensorflow/compiler/mlir/tensorflow:error_util",
        "//tensorflow/compiler/mlir/tensorflow:mlir_import_options",
        "//tensorflow/compiler/mlir/tensorflow:mlir_roundtrip_flags",
        "//tensorflow/compiler/mlir/tensorflow:tf_dialect_lib",
        "//tensorflow/compiler/mlir/tensorflow:translate_lib",

build:rocm --config=no_tfrt

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/attribute_utils.h"
#include "tensorflow/compiler/mlir/lite/utils/validators.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.h"

  """set TF_CUDA_CLANG action_env.

  Args:
    environ_cp: copy of the os.environ.
  """
  question = 'Do you want to use clang as CUDA compiler?'
  yes_reply = 'Clang will be used as CUDA compiler.'
  no_reply = 'nvcc will be used as CUDA compiler.'
  set_action_env_var(
      environ_cp,
      'TF_CUDA_CLANG',
      None,
      True,
      question=question,
      yes_reply=yes_reply,

					"kotlin-dsl-provider-plugins"
				]
			},
			{
				"parallelizationMethod":{
					"name":"TestDistribution"
				},
				"subprojects":[
					"plugin-use",
					"native",
					"java-compiler-plugin",
					"file-temp",
					"concurrent",
					"functional",
					"test-suites-base",
					"internal-testing",
					"io",
					"testing-jvm-infrastructure",
					"cli"
				]
			},
			{

==== Configuring Kotlin compiler options with the `kotlin-dsl` plugin applied

If you are configuring custom Kotlin compiler options on a project with the <<kotlin_dsl.adoc#sec:kotlin-dsl_plugin, kotlin-dsl>> plugin applied you might encounter a breaking change.

#include "absl/container/flat_hash_set.h"
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "flatbuffers/vector.h"  // from @flatbuffers
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_utils.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/platform/types.h"

		}
	}
}

// Sink variables are here to force the compiler to not elide
// seemingly useless work in benchmarks and allocation tests. If you
// were to just `_ = foo()` within a test function, the compiler could
// correctly deduce that foo() does nothing and doesn't need to be
// called. By writing results to a global variable, we hide that fact
// from the compiler and force it to keep the code under test.
var (

         *             const val K = 1
         *         }
         *     }
         *
         * the compiler reports "Variable 'K' must be initialized". This happens because there is no guarantee that the companion object
         * was initialized at the time when we use `C.K` for the enum entry `ONE`. To avoid this type of compiler errors, we don't shorten

	if cap(state.blockDebug) < f.NumBlocks() {
		state.blockDebug = make([]BlockDebug, f.NumBlocks())
	} else {
		// This local variable, and the ones like it below, enable compiler
		// optimizations. Don't inline them.
		b := state.blockDebug[:f.NumBlocks()]
		for i := range b {
			b[i] = BlockDebug{}
		}
	}

	// A list of slots per Value. Reuse the previous child slices.