auto build_result = BuildHloFromGraph(*graph, /*use_output_shapes=*/true);
  ASSERT_FALSE(build_result.ok());
  EXPECT_THAT(build_result.message(),
              HasSubstr("op operand type 'tensor<2x3x4x5xi32>' and result type "
                        "'tensor<1xi32>' are cast incompatible"));
}

}  // namespace

// Tests that a float `stablehlo.constant` is not converted into `tfl.qconst`.

// CHECK-LABEL: func @float_constant
func.func @float_constant() -> tensor<1x2x4x5xf32> {
  %0 = stablehlo.constant() {value = dense<1.0> : tensor<1x2x4x5xf32>} : () -> tensor<1x2x4x5xf32>
  return %0 : tensor<1x2x4x5xf32>
}

// CHECK: stablehlo.constant
// CHECK-NOT: tfl.pseudo_qconst
// CHECK-NOT: tfl.pseudo_const
// CHECK-NOT: arith.constant

  %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<4x5x1x?x3xf32>, tensor<1x1x3x5xf32>) -> tensor<4x5x1x?x5xf32>
  func.return %0 : tensor<4x5x1x?x5xf32>
}

// -----

func.func @testBatchMatMulV2InvalidPartiallyKnownMatmulDim(%lhs: tensor<4x5x1x?x3xf32>, %rhs: tensor<1x1x3x5xf32>) -> (tensor<4x5x1x?x3xf32>) {

}

func.func @LRN(%arg0: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> {
  %0 = "tf.LRN"(%arg0) {depth_radius = 5 :i64, bias = 1.0 :f32, alpha = 1.0 : f32, beta = 0.5 :f32} : (tensor<2x3x4x5xf32>) -> (tensor<2x3x4x5xf32>)
  func.return %0: tensor<2x3x4x5xf32>

  // CHECK-LABEL: LRN

}

// CHECK-LABEL: func @BiasAddGrad_NHWC
func.func @BiasAddGrad_NHWC(%arg0: tensor<2x3x4x5xf32>) -> tensor<5xf32> {
  // CHECK: "tf.Const"() <{value = dense<[0, 1, 2]> : tensor<3xi64>}>
  // CHECK: "tf.Sum"({{.*}}) <{keep_dims = false}>

  %0 = "tf.BiasAddGrad"(%arg0) {data_format = "NHWC"} : (tensor<2x3x4x5xf32>) -> tensor<5xf32>
  func.return %0 : tensor<5xf32>
}

// CHECK-LABEL: func @BiasAddGrad_NCHW

  %0 = "tf.Const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
  %1 = "tf.Const"() {value = dense<[2, 0, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
  %2 = "tf.Transpose"(%arg0, %0) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>
  %3 = "tf.Transpose"(%2, %1) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<4x1x4x8xf32>

  func.return %3 : tensor<4x1x4x8xf32>

// CHECK:           })
// CHECK-SAME:        -> tensor<2x7x3x5xf32>
// CHECK:           [[COUNT:%.+]] = mhlo.constant dense<4.000000e+00> : tensor<f32>
// CHECK:           [[DIV_RESULT:%.+]] = chlo.broadcast_divide [[DIVIDEND]], [[COUNT]]
// CHECK-SAME:        broadcast_dimensions = array<i64>
// CHECK-SAME:        -> tensor<2x7x3x5xf32>
// CHECK:           [[CONV16:%.+]] = mhlo.convert [[DIV_RESULT]]