// This file implements the Punycode algorithm from RFC 3492.

import (
	"fmt"
	"net/http/internal/ascii"
	"strings"
	"unicode/utf8"
)

// These parameter values are specified in section 5.
//
// All computation is done with int32s, so that overflow behavior is identical
// regardless of whether int is 32-bit or 64-bit.
const (
	base        int32 = 36
	damp        int32 = 700
	initialBias int32 = 72
	initialN    int32 = 128

  // Import the given XlaComputation into the parent module. Returns the given
  // generated function.
  absl::StatusOr<mhlo::TupleOp> ImportXlaComputation(
      xla::XlaComputation& computation);

  // Prepares OpKernelContext params common to all the ops.
  // Emits an error on failure.
  mlir::LogicalResult PrepareParams();

  // Given the required_consts, it will fill the 3 output vectors with

      old_parallel_execute, new_parallel_execute);

  // Extract inputs for each block of the parallel_execute op. The i-th
  // element in the list represents the input lists to TPU computation for
  // i-th logical core.
  llvm::SmallVector<llvm::SmallVector<mlir::Value, 4>, 4> input_list;
  builder->setInsertionPoint(*new_parallel_execute);
  auto result = tensorflow::ExtractInputsForLogicalDevices(

    bool has_ref_vars, bool may_alias_resource_update) {
  XlaCompiler::CompileOptions compile_options;
  compile_options.is_entry_computation = true;
  // Optimization: where possible, have the computation return a naked array
  // rather than a one-element tuple.
  compile_options.always_return_tuple = false;
  compile_options.alias_resource_update =
      !has_ref_vars && may_alias_resource_update;

   *     ExecutionException} is thrown <a
   *     href="https://github.com/google/guava/wiki/CachesExplained#interruption">even if
   *     computation was interrupted by an {@code InterruptedException}</a>.)
   * @throws UncheckedExecutionException if an unchecked exception was thrown while loading the
   *     value

}

absl::StatusOr<ResourceMgr*> GetResourceMgrForDeviceCompiler(
    const OpKernelContext& ctx, const DeviceType& device_type) {
  // We store information about the JIT-compiled XLA computation in the
  // ResourceMgr. The DeviceCompiler (which contains the DeviceCompilationCache)
  // is stored in the tfrt_global ResourceMgr for TPU and the Device ResourceMgr

	// Since Newton's Method doubles the number of correct digits at each
	// iteration, we need at least log_2(prec) steps.
	steps := int(math.Log2(prec))

	// Initialize values we need for the computation.
	two := new(big.Float).SetPrec(prec).SetInt64(2)
	half := new(big.Float).SetPrec(prec).SetFloat64(0.5)

	// Use 1 as the initial estimate.
	x := new(big.Float).SetPrec(prec).SetInt64(1)

   * required, to have the following properties:
   *
   * <ul>
   *   <li>Its execution does not cause any observable side effects.
   *   <li>The computation is <i>consistent with equals</i>; that is, {@link Objects#equal
   *       Objects.equal}{@code (a, b)} implies that {@code predicate.apply(a) ==
   *       predicate.apply(b))}.
   * </ul>
   *

 private:
  explicit FoldAdaptor(Operation* operation) : operation_(operation) {}
  Operation* const operation_;
};

// APSInt provides operators which APInt does not, so allow for converting
// to APSInt for computation. Only APInts can be directly read from
// element attributes.
static const APFloat& AddSign(const APFloat& v) { return v; }
static APSInt AddSign(const APInt& v) { return APSInt(v); }

template <typename ResultType>

}

def TfDevice_ReplicateOp : TfDevice_Op<"replicate",
    [SingleBlockImplicitTerminator<"ReturnOp">, AttrSizedOperandSegments]> {
  let summary = "Wraps an N-way replicated computation.";

  let description = [{
The region held by this operation represents a computation that is replicated
across multiple devices. The number of replications is based on the `n`
attribute. Explicit devices can be populated in the `devices` attribute, and it