func.func @slice_mhlo_sizes(%arg0: tensor<1x1024x4xf32>, %arg1: tensor<3xi32>) -> tensor<1x512x4xf32> {
  // CHECK-NOT: "tf.Slice"
  %0 = "mhlo.constant"() {value = dense<[1, 512, 4]> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tf.Slice"(%arg0, %arg1, %0) : (tensor<1x1024x4xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x512x4xf32>
  func.return %1 : tensor<1x512x4xf32>
}

// -----

// CHECK: %[[ADD:.+]] = stablehlo.add %[[CONV]], %[[BIAS_CONST]] : tensor<1x5x5x4xf32>
// CHECK: %[[MAX:.+]] = stablehlo.maximum %[[ADD]], %[[ZERO_CONST]] : tensor<1x5x5x4xf32>
// CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %[[MAX]], dims = [0, 3, 1, 2] : (tensor<1x5x5x4xf32>) -> tensor<1x4x5x5xf32>
// CHECK: return %[[TRANSPOSE_1]]

// -----

  // CHECK-LABEL: test_batch_matmul_to_einsum
  // CHECK: "tf.Einsum"(%arg0, %arg1) <{equation = "...mk,...kn->...mn"}> : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>
  %0 = "tf.BatchMatMul"(%arg0, %arg1) {adj_x = false, adj_y = false} : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>
  func.return %0: tensor<1x2x4xf32>
}

  %1 = mhlo.multiply %0, %cst1 : tensor<1x1x2x4xf32>
  // CHECK:      return %[[RES]] : tensor<1x1x2x4xf32>
  func.return %1 : tensor<1x1x2x4xf32>
}

// CHECK-LABEL: @foldBroadcastInDimBeforeMulOp_bcast_dim_2D_float
func.func @foldBroadcastInDimBeforeMulOp_bcast_dim_2D_float() -> (tensor<1x2x2x3xf32>) {

func.func @add_with_activation_transpose_permutation_mismatch(
      %arg0: tensor<1x2x3x4xf32>) -> tensor<1x3x2x4xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<1x3x2x4xf32>
  %1 = stablehlo.transpose %arg0, dims = [0, 2, 1, 3] : (tensor<1x2x3x4xf32>) -> tensor<1x3x2x4xf32>
  %2 = stablehlo.add %1, %0 : tensor<1x3x2x4xf32>
  return %2 : tensor<1x3x2x4xf32>
}
// CHECK: %[[TRANSPOSE_0:.+]] = stablehlo.transpose

  // CHECK: %[[LHS_1:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#0, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x5x4xf32>, tensor<2xi64>) -> tensor<5x4xf32>
  // CHECK: %[[LHS_2:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#1, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x5x4xf32>, tensor<2xi64>) -> tensor<5x4xf32>

      return %0 : tensor<1x1x4xf32>
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kModuleXlaCallModuleOpWithDefaultQuantizationMethod);
  ASSERT_TRUE(module_op);

func.func @mean_4d_no_keepdim(%arg0: tensor<1x48x48x512xf32>) -> tensor<1x512xf32> {
  %cst = arith.constant dense<[1, 2]> : tensor<2xi32>
  %0 = "tfl.mean"(%arg0, %cst) {keep_dims = false} : (tensor<1x48x48x512xf32>, tensor<2xi32>) -> tensor<1x512xf32>
  func.return %0 : tensor<1x512xf32>
}

// CHECK:       func @mean_4d_no_keepdim([[VAL_0:%.*]]: tensor<1x48x48x512xf32>) -> tensor<1x512xf32> {

      return %0 : tensor<1x1x4xf32>
    }
  )mlir";

  const OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kXlaCallModuleOpWithQuantizationMethodAttr);
  ASSERT_TRUE(module_op);

  func::FuncOp main_fn = FindMainFuncOp(*module_op);

  %flow_1 = "tf.TensorArrayWriteV3"(%handle, %index, %value, %flow) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<2x!tf_type.resource<tensor<1x512xf32>>>, tensor<i32>, tensor<1x512xf32>, tensor<f32>) -> tensor<f32>