// CHECK-NEXT:  for node {{[{][{]node Add[}][}]}}

func.func @main() {
  tf_executor.graph {
    %0 = tf_executor.island wraps "tf._TPUReplicate"() {computation = @foo, Tinputs = [], Tbroadcast_inputs = [], NumVariables = 0, Tguaranteed_constants = [], output_types = []} : () -> () loc("_TPUReplicate")
    tf_executor.fetch
  }
  func.return
}

func.func @foo() {

  %cst_3 = "tf.Const"() {value = dense<4> : tensor<1xi32>} : () -> tensor<1xi32>
  %0 = "tf.XlaReduceWindow"(%arg0, %arg1, %cst_0, %cst_1, %cst_2, %cst_3, %cst) {computation = @sum_reducer} : (tensor<7xf32>, tensor<f32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1x2xi32>) -> tensor<10xf32>
  func.return %0 : tensor<10xf32>
}

XlaCompiler::CompileOptions GetCompileOptions(bool for_pjrt = false) {
  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;
  if (for_pjrt) {
    compile_options.use_tuple_arg = false;
    compile_options.always_return_tuple = true;
  }

#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/graph/while_context.h"

// Utility functions for constructing while loop gradients

namespace tensorflow {

// Adds the gradient computation for the while loop associated with
// `while_ctx`. `grad_inputs` are the partial derivatives w.r.t. the loop
// outputs, i.e. the exit nodes.  The partial derivatives w.r.t. the loop

namespace tensorflow {
namespace tfcompile {

using QuantizeXlaFn = std::function<Status(const tf2xla::Config& config,
                                           xla::XlaComputation* computation)>;

// Set the static quantization function to the `fn` if it hasn't been set.
// Return false if the static function has been set.
bool RegisterQuantizeFn(const QuantizeXlaFn& fn);

}  // namespace tfcompile

 */

package com.google.common.collect;

import com.google.common.annotations.GwtCompatible;
import javax.annotation.CheckForNull;

/**
 * Wraps an exception that occurred during a computation.
 *
 * @author Bob Lee
 * @since 2.0
 * @deprecated This exception is no longer thrown by {@code com.google.common}. Previously, it was

 */

package com.google.common.collect;

import com.google.common.annotations.GwtCompatible;
import javax.annotation.CheckForNull;

/**
 * Wraps an exception that occurred during a computation.
 *
 * @author Bob Lee
 * @since 2.0
 * @deprecated This exception is no longer thrown by {@code com.google.common}. Previously, it was

}

// Represents an entry in the XLA compile cache.
message XlaSerializedCacheEntry {
  // Used to uniqely identify this entry in its persisted representation.
  XlaSerializedCacheKey key = 1;

  // The computation (HLO) that compilation was done for. It is correlated to
  // the input TF graph so we can use it to fingerprint the compiled binary. We
  // serialize this rather than the input graphdef because it provides a

namespace tf2xla {
namespace internal {

// Legalize the given MLIR module to XLA HLO using a combination of the MLIR
// Bridge and XlaBuilder
absl::StatusOr<XlaCompilationResult> LegalizeTfToHlo(
    const tpu::MlirToHloArgs& computation,
    const tpu::TPUCompileMetadataProto& metadata, bool use_tuple_args,
    llvm::StringRef device_type,
    XlaShapeLayoutHelpers::ShapeDeterminationFns shape_determination_fns,

//
// Like qcasts, a dcast is allowed to have both its operand and result
// as non quantized types. This facilitates transformations and marks edges
// where the computation must be carried out in the expressed type.
//
// Especially early in transformation, it is common to have dcasts on
// all operands to ops that must operate with the expressed type (typically