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>

      .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

// CHECK: func @main(%arg0: tensor<?x19x19x3xf32>) -> tensor<?x9x9x4xf32>
func.func @main(%arg0: tensor<?x19x19x3xf32>) -> tensor<?x9x9x4xf32> {
  %cst = arith.constant dense<1.0> : tensor<4xf32>
  %cst_3 = arith.constant dense<2.0> : tensor<4x3x3x3xf32>

// CHECK-LABEL: func @main
func.func @main(%arg0: tensor<1xf32>) -> tensor<1xf32>
attributes  {tf.entry_function = {inputs = "input", outputs = "output_node"}} {
  %0 = arith.constant dense<2.000000e+00> : tensor<f32>
  %1 = arith.constant dense<1.000000e+00> : tensor<f32>
  %2 = "tf.AddV2"(%arg0, %1) {T = "tfdtype$DT_FLOAT", device = "", name = "StatefulPartitionedCall/add"} : (tensor<1xf32>, tensor<f32>) -> tensor<1xf32>

limitations under the License.
==============================================================================*/

#include <memory>
#include <utility>

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/Pass/PassRegistry.h"  // from @llvm-project

limitations under the License.
==============================================================================*/

#include "tensorflow/compiler/mlir/tf2xla/transforms/xla_legalize_targets.h"

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project
#include "mlir/Dialect/Tensor/IR/Tensor.h"  // from @llvm-project

func.func @mixed_no_constant_folding() -> (tensor<f32>) {
  // CHECK:      %[[CST0:.+]] = arith.constant dense<0.000000e+00>
  // CHECK-NEXT: %[[CST1:.+]] = arith.constant dense<0.000000e+00>
  // CHECK-NEXT: "vhlo.add_v1"(%[[CST0]], %[[CST1]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %cst_0 = arith.constant dense<0.000000e+00> : tensor<f32>
  %cst_1 = arith.constant dense<0.000000e+00> : tensor<f32>
  %0 = stablehlo.add %cst_0, %cst_1 : tensor<f32>

module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>