attributes {tf.entry_function = {inputs = "input", outputs = "Conv2D"}} {
  %cst = arith.constant dense<[0, 1, 2, 3]> : tensor<4xi32>
  %cst_0 = arith.constant dense<1.000000e+00> : tensor<f32>
  %cst_1 = arith.constant dense<0.000000e+00> : tensor<8xf32>
  %cst_2 = arith.constant dense<0.000000e+00> : tensor<8x3x3x3xf32>

// PerTensorWeightOnly-LABEL: QuantizeConv2D
// BLOCK-LABEL: QuantizeConv2D
func.func @QuantizeConv2D(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x64xf32> {
  %w = arith.constant dense<1.270000e+02> : tensor<64x3x3x3xf32>
  %b = arith.constant dense<-1.23697901> : tensor<64xf32>

// CHECK-DAG:       %[[CST_0:.*]] = arith.constant dense<1.000000e+00> : tensor<f32>
// CHECK-DAG:       %[[CST_1:.*]] = arith.constant dense<0.000000e+00> : tensor<f32>
// CHECK-DAG:       %[[CST_2:.*]] = arith.constant dense<0> : tensor<i32>
// CHECK-DAG:       %[[VAL_0:.*]] = "tfl.while"(%[[CST_2]]) ({
// CHECK-DAG:       ^bb0(%[[A2_COND:.*]]: tensor<i32>):
// CHECK-DAG:         %[[CST_3:.*]] = arith.constant dense<3> : tensor<i32>

func.func @testConcatOffsetWithZeros() -> (tensor<3xi32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>) {
  %concat_dim = arith.constant dense<1> : tensor<i32>
  %shape0 = arith.constant dense<0> : tensor<3xi32>
  %shape1 = arith.constant dense<[0, 3, 0]> : tensor<3xi32>
  %shape2 = arith.constant dense<[0, 5, 0]> : tensor<3xi32>
  %shape3 = arith.constant dense<0> : tensor<3xi32>

  // CHECK-DAG: [[OFFSET_0:%.*]] = "tf.Const{{.*}} dense<0> : tensor<3xi32>

  // CHECK: [[CST:%[^ ]*]] = arith.constant dense<1> : tensor<i32>
  // CHECK: "tf.WhileRegion"([[ARG0]], [[ARG1]])
  // CHECK: (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) (
    // cond
    {
    ^bb0(%condArg0: tensor<*xi32>, %condArg1: tensor<*xf32>):
      %0 = "arith.constant" () {value = dense<0> : tensor<i32>} : () -> tensor<i32> loc("Const")

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %cst_0 = arith.constant dense<1.0> : tensor<5x384xf32>
    %cst_1 = arith.constant dense<1.0> : tensor<5x384xf32>

#include "tensorflow/compiler/mlir/lite/utils/constant_utils.h"

#include <complex>
#include <cstdint>
#include <string>
#include <vector>

#include "absl/status/status.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributeInterfaces.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project

  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32>
  %w_1 = arith.constant dense<127.0> : tensor<4096x1x1x1xf32>
  %w_2 = arith.constant dense<127.0> : tensor<128x1x1x1xf32>
  %b = arith.constant dense<127.0> : tensor<2048x1x1x1xf32>

func.func @testDilatedConvWithPad(%arg0: tensor<1x128x128x3xf32>, %arg1: tensor<5x5x3x8xf32>, %arg2: tensor<8xf32>) -> tensor<1x128x128x8xf32> {
  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %cst_0 = arith.constant dense<4> : tensor<2x2xi32>
  %cst_1 = arith.constant dense<0> : tensor<2x2xi32>
  %cst_2 = arith.constant dense<0> : tensor<4x2xi32>

// -----

// Test for case with import and find ops.
func.func @hashtable_import_then_find(%arg0: tensor<5x!tf_type.string>) -> tensor<*xi64> {
  %cst = arith.constant dense<["emerson", "lake", "palmer"]> : tensor<3x!tf_type.string>
  %cst_0 = arith.constant dense<-1> : tensor<i64>
  %cst_1 = arith.constant dense<[0, 1, 2]> : tensor<3xi64>