// nolint: revive, stylecheck
	TAG Variable = "TAG"

	// VARIANT is the Docker variant to be used for images.
	// nolint: revive, stylecheck
	VARIANT Variable = "VARIANT"

	// PULL_POLICY is the image pull policy to use when rendering templates.
	// nolint: revive, stylecheck
	PULL_POLICY Variable = "PULL_POLICY"

	// ECHO_IMAGE is the image to use when deploying echo services.
	// nolint: golint, revive, stylecheck

#define TEST_NAME {{TFCOMPILE_NAME}}Test   // NOLINT(whitespace/braces)
#define BM_NAME   BM_{{TFCOMPILE_NAME}}    // NOLINT(whitespace/braces)
// clang-format on

namespace tensorflow {
namespace tfcompile {
namespace {

void zero_buffers(XlaCompiledCpuFunction* computation) {
  for (int i = 0; i < computation->num_args(); ++i) {
    memset(computation->arg_data(i), 0, computation->arg_size(i));

// clang-format off
#include "{{TFCOMPILE_HEADER}}"  // NOLINT(whitespace/braces)
// clang-format on

#include "tensorflow/compiler/aot/benchmark.h"
#include "unsupported/Eigen/CXX11/Tensor"

// Macros that expand to tokens based on the entry point name.
// clang-format off
#define CPP_CLASS {{TFCOMPILE_CPP_CLASS}}  // NOLINT(whitespace/braces)
// clang-format on

namespace tensorflow {

#define TEST_NAME {{TFCOMPILE_NAME}}Test   // NOLINT(whitespace/braces)
#define BM_NAME   BM_{{TFCOMPILE_NAME}}    // NOLINT(whitespace/braces)
// clang-format on

namespace tensorflow {
namespace tfcompile {
namespace {

void zero_buffers(XlaCompiledCpuFunction* computation) {
  for (int i = 0; i < computation->num_args(); ++i) {
    memset(computation->arg_data(i), 0, computation->arg_size(i));
  }
}

          mappedTo.writeUtf8CodePoint(readHexadecimalUnsignedLong().toInt())
        }
      }

      TYPE_DISALLOWED, TYPE_DISALLOWED_STD3_VALID, TYPE_IGNORED, TYPE_VALID -> Unit

      else -> throw IOException("unexpected type")
    }

    skipRestOfLine()

    result +=
      Mapping(
        sourceCodePoint0.toInt(),
        sourceCodePoint1.toInt(),
        type,
        mappedTo.readByteString(),

  )
}

/**
 * If [mapping] qualifies to be encoded as [MappedRange.InlineDelta] return new instance, otherwise null.
 * An [MappedRange.InlineDelta] must be a mapping from a single code-point to a single code-point with a difference
 * that can be represented in 2^18-1.
 */
internal fun inlineDeltaOrNull(mapping: Mapping): MappedRange.InlineDelta? {
  if (mapping.hasSingleSourceCodePoint) {

   * For example, the tags in this schema are 0 and 1:
   *
   * ```
   * Point ::= SEQUENCE {
   *   x [0] INTEGER OPTIONAL,
   *   y [1] INTEGER OPTIONAL
   * }
   * ```
   *
   * You may also specify a tag class like [DerHeader.TAG_CLASS_APPLICATION]. The default tag class
   * is [DerHeader.TAG_CLASS_CONTEXT_SPECIFIC].
   *
   * ```
   * Point ::= SEQUENCE {
   *   x [APPLICATION 0] INTEGER OPTIONAL,

    const std::string& regex = spec.regex();
    if (regex.empty()) return nullptr;

    return std::make_unique<RE2>(regex);  // NOLINT
  }

  // Regex object used for matching against a lifted function's name.
  std::unique_ptr<RE2> match_regex_;  // NOLINT
};

// Converts `Method` to a single-line textproto representation. Returns
// `failure()` when converting to textproto failed.

using ::mlir::tf_type::OperandShapeIterator;       // NOLINT
using ::mlir::tf_type::ResourceType;               // NOLINT
using ::mlir::tf_type::ResultShapeIterator;        // NOLINT
using ::mlir::tf_type::ResultShapeRange;           // NOLINT
using ::mlir::tf_type::StringType;                 // NOLINT
using ::mlir::tf_type::TensorFlowRefType;          // NOLINT
using ::mlir::tf_type::TensorFlowType;             // NOLINT

// This all moved under tensorflow/core/ir/types and these using declaration are
// to help with the transition.
using mlir::tf_type::FuncAttr;         // NOLINT
using mlir::tf_type::PlaceholderAttr;  // NOLINT
using mlir::tf_type::ShapeAttr;        // NOLINT
using mlir::tf_type::TensorProtoAttr;  // NOLINT

}  // end namespace TF
}  // end namespace mlir