#include <string>

#include "llvm/Support/MemoryBuffer.h"
#include "llvm/Support/SMLoc.h"
#include "llvm/Support/SourceMgr.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/SCF/IR/SCF.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project

    mlir::OpBuilder op_builder(&module.getBodyRegion());
    module.walk([&](mlir::arith::ConstantOp const_op) {
      MaybeUnfoldLargeSplatConstant(&op_builder, const_op);
    });
  }

 private:
  void MaybeUnfoldLargeSplatConstant(mlir::OpBuilder* op_builder,
                                     mlir::arith::ConstantOp const_op) const {
    auto splat_elements_attr =

module {
  func.func @main(%arg0 : tensor<!tf_type.resource<tensor<4096xf32>>>,
      %arg1 : tensor<*x!tf_type.variant>) {
    %cst_0 = arith.constant dense<2> : tensor<i64>
    %cst_1 = arith.constant dense<0> : tensor<i32>
    %0 = "tf.RepeatDataset"(%arg1, %cst_0) {device = "",
      output_shapes = [#tf_type.shape<?>],

)

var (
	configRRSParityMD = NewGaugeMD(configRRSParity,
		"Reduced redundancy storage class parity")
	configStandardParityMD = NewGaugeMD(configStandardParity,
		"Standard storage class parity")
)

// loadClusterConfigMetrics - `MetricsLoaderFn` for cluster config
// such as standard and RRS parity.
func loadClusterConfigMetrics(ctx context.Context, m MetricValues, c *metricsCache) error {

#ifndef TENSORFLOW_COMPILER_MLIR_TENSORFLOW_DIALECT_REGISTRATION_H_
#define TENSORFLOW_COMPILER_MLIR_TENSORFLOW_DIALECT_REGISTRATION_H_

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/ControlFlow/IR/ControlFlow.h"  // from @llvm-project
#include "mlir/Dialect/Func/Extensions/AllExtensions.h"  // from @llvm-project

  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

// CHECK:           %[[VAL_1:.*]] = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
// CHECK:           %[[VAL_2:.*]] = "tfl.transpose"(%[[VAL_0]], %[[VAL_1]]) : (tensor<1x3x6x6xf32>, tensor<4xi32>) -> tensor<1x6x6x3xf32>
// CHECK:           %[[VAL_3:.*]] = arith.constant dense<0> : tensor<4x2xi32>

      .replaceAllUsesWith(cloned_const_op->getResult(0));
}

bool IsConstOrQConstInt(Operation* op) {
  if (!llvm::isa<TFL::ConstOp, TFL::QConstOp, arith::ConstantOp>(op))
    return false;

  if (auto arith_const_op = dyn_cast_or_null<arith::ConstantOp>(op)) {
    // arith ConstOp path.
    auto type =
        mlir::cast<ShapedType>(arith_const_op.getType()).getElementType();

# Erasure code sizing guide

## Toy Setups

Capacity constrained environments, MinIO will work but not recommended for production.

| servers | drives (per node) | stripe_size | parity chosen (default) | tolerance for reads (servers) | tolerance for writes (servers) |
|--------:|------------------:|------------:|------------------------:|------------------------------:|-------------------------------:|

==============================================================================*/
#include "tensorflow/compiler/mlir/lite/quantization/lite/tfl_to_std.h"

#include "llvm/Support/Casting.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project