}

//===----------------------------------------------------------------------===//
// EinsumOp
//===----------------------------------------------------------------------===//

// Verifies that,
// * Arity of the op is at most two.
//
// TODO(hinsu): Verify einsum equation attribute.
LogicalResult EinsumOp::verify() {
  EinsumOp op = *this;
  if (op.getN() > 2) {

// CHECK-DAG:       [[VAL_35:%.*]] = arith.constant dense<0> : tensor<1xi64>
// CHECK-DAG:       [[VAL_36:%.*]] = arith.constant dense<1> : tensor<1xi64>
// CHECK:           [[VAL_37:%.*]] = "tf.Slice"([[VAL_3]], [[VAL_35]], [[VAL_36]]) : (tensor<2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<1xf32>
// CHECK-DAG:       [[VAL_38:%.*]] = arith.constant dense<1> : tensor<1xi64>
// CHECK-DAG:       [[VAL_39:%.*]] = arith.constant dense<1> : tensor<1xi64>

// CHECK-DAG:       [[VAL_8:%.*]] = arith.constant dense<0.000000e+00> : tensor<16x16xf32>
// CHECK-DAG:       [[VAL_9:%.*]] = arith.constant dense<0.000000e+00> : tensor<16x8xf32>
// CHECK-DAG:       [[VAL_10:%.*]] = arith.constant dense<0.000000e+00> : tensor<16xf32>
// CHECK-DAG:       [[VAL_11:%.*]] = arith.constant dense<0.000000e+00> : tensor<1x16xf32>
// CHECK-DAG:       [[VAL_12:%.*]] = arith.constant dense<0.000000e+00> : tensor<8x16xf32>

  %size_splits_0 = arith.constant dense<[8, 8]> : tensor<2xi32>
  %split_dim_0 = arith.constant dense<0> : tensor<i32>
  %0, %1 = "tfl.split_v"(%arg0, %size_splits_0, %split_dim_0) {num_splits = 2 : i32} : (tensor<16x4xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<8x4xf32>, tensor<8x4xf32>)

  %size_splits_1 = arith.constant dense<[2, 2]> : tensor<2xi32>
  %split_dim_1 = arith.constant dense<1> : tensor<i32>

      %add = "tf.Add"(%barg0, %barg0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
      %one = arith.constant dense<1> : tensor<i32>
      %sub = "tf.Sub"(%barg1, %one) : (tensor<i32>, tensor<i32>) -> tensor<i32>
      %dummy0 = arith.constant dense<7> : tensor<i32>
      %dummy1 = arith.constant dense<3.0> : tensor<f32>
      "tf.Yield"(%add, %sub, %dummy0, %dummy1) : (tensor<*xf32>, tensor<i32>, tensor<i32>, tensor<f32>) -> ()
    }

#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/ADT/StringSwitch.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/Attributes.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project

// and at most 10 arguments in Combine. Please contact
// ******@****.*** if you need more.
// Please note that the number of arguments to Combine is limited
// by the maximum arity of the implementation of tr1::tuple which is
// currently set at 10.

#ifndef GTEST_INCLUDE_GTEST_INTERNAL_GTEST_PARAM_UTIL_GENERATED_H_
#define GTEST_INCLUDE_GTEST_INTERNAL_GTEST_PARAM_UTIL_GENERATED_H_

      %arg11 : tensor<!tf_type.resource<tensor<512xf32>>>
      ) {
    %cst_0 = arith.constant dense<0.1> : tensor<f32>
    %cst_2 = arith.constant dense<2> : tensor<i64>
    %cst_12 = arith.constant dense<0> : tensor<i32>
    %cst_13 = arith.constant dense<> : tensor<0x!tf_type.string>
    %51 = "tf.RepeatDataset"(%arg1, %cst_2) {device = "",

// CHECK-DAG: %[[BROADCAST_DIM:.+]] = arith.constant dense<{{\[3, 2, 1, 1, 4\]}}> : tensor<5xi32>
// CHECK-DAG: %[[EXPAND_DIM1:.+]] = arith.constant dense<3> : tensor<1xi32>
// CHECK-DAG: %[[EXPAND_DIM0:.+]] = arith.constant dense<2> : tensor<1xi32>
// CHECK-DAG: %[[PERM:.+]] = arith.constant dense<{{\[1, 0, 2\]}}> : tensor<3xi32>

#include <utility>

#include "absl/algorithm/container.h"
#include "absl/log/check.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Support/Debug.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantOps.h"  // from @llvm-project  // NOLINT: Required to register quantization dialect.