// type.
  if (!updated.hasRank()) return false;

  // If the original type doesn't have a rank, then refine as the updated type
  // has a rank.
  if (!original.hasRank()) return true;

  // Both types must have the same rank.
  if (original.getRank() != updated.getRank()) return false;

  // Refine if the updated type is bounded.
  return IsBounded(updated);

namespace TF {

// Returns whether type can be further refined.
bool CanBeRefined(Type type);

// Returns a new arg type based on the shape and element type. If there are
// dynamic bounds attribute to the arg, update the bounds based on the shape
// as well.
Type GetNewArgType(Type old_arg_type, ArrayRef<int64_t> shape,
                   Type element_type, mlir::MLIRContext* context);

using tensorflow::shape_inference::InferenceContext;
using tensorflow::shape_inference::ShapeHandle;

namespace mlir {
namespace TF {
namespace {

// Compute a refined type between two types `lhs` and `rhs`, the result type
// is always more refined (i.e. has more static information) than `lhs`
// This method will actually merge the information contained in the
// types, it is capable of refining:

  parser.emitError(parser.getNameLoc()) << "unknown TFL type: " << data_type;
  return nullptr;
}

void TFLDialect::initialize() {
  addOperations<
#define GET_OP_LIST
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.cc.inc"
      >();
  addAttributes<
#define GET_ATTRDEF_LIST
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops_attrdefs.cc.inc"
      >();
  addInterfaces<TensorFlowLiteInlinerInterface,

Ben Leggett <******@****.***> 1716316321 -0400

		var msg string
		if f.name == "" {
			msg = f.sprintf("flag redefined: %s", name)
		} else {
			msg = f.sprintf("%s flag redefined: %s", f.name, name)
		}
		panic(msg) // Happens only if flags are declared with identical names
	}
	if pos := f.undef[name]; pos != "" {
		panic(fmt.Sprintf("flag %s set at %s before being defined", name, pos))
	}
	if f.formal == nil {
		f.formal = make(map[string]*Flag)

 */

package org.jetbrains.kotlin.analysis.api.platform

/**
 * An **engine service** as defined by the Platform Interface (see the README).
 *
 * Engine services do not need to be implemented by a platform. Quite the contrary, they are implemented by the Analysis API engine and
 * intended to support platform implementations. They are defined in the Platform Interface, as opposed to the user-facing Analysis API,

	// CAName defines the name for the CA on which this certificate depends
	CAName string

	// CABaseName defines the base name for the CA that should be used for certificate renewal
	CABaseName string

	// readwriter defines a CertificateReadWriter to be used for certificate renewal
	readwriter certificateReadWriter

	// certConfigMutators holds the mutator functions that can be applied to the input cert config object

                            A set of common keys MAY be defined by the API in the future. To avoid
                            any ambiguity, implementation-specific definitions MUST use
                            domain-prefixed names, such as `example.com/my-custom-option`.
                            Un-prefixed names are reserved for key names defined by Gateway API.

        EnvVariableRead.getEnv("CI")                        | "null"     | "defined" | "defined"
        EnvVariableRead.getEnvGet("CI")                     | "null"     | "defined" | "defined"
        EnvVariableRead.getEnvGetOrDefault("CI", "default") | "default"  | "defined" | "defined"
        EnvVariableRead.getEnvContainsKey("CI")             | "false"    | "defined" | "true"
    }

    @Issue("https://github.com/gradle/gradle/issues/19710")