#
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<1xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "a"]},
  # CHECK-SAME:   %arg1: tensor<2xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "b"]},
  # CHECK-SAME:   %arg2: tensor<3xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "c"]},

// CHECK:           "mhlo.dot_general"({{.*}}, {{.*}}) <{
// CHECK-SAME:        lhs_batching_dimensions = [0]
// CHECK-SAME:        rhs_batching_dimensions = [0]
// CHECK-SAME:        lhs_contracting_dimensions = [2]
// CHECK-SAME:        rhs_contracting_dimensions = [1]

  // CHECK: %[[CONV2D_BACKPROP:[0-9]*]] = "tf.Conv2DBackpropFilter"
  // CHECK-SAME: (%[[IN_TRANSPOSE]], %[[FILTER_PERM]], %[[OUT_BP_TRANSPOSE]])
  // CHECK-SAME: data_format = "NCHW"
  // CHECK-SAME: dilations = [1, 4, 2, 3]
  // CHECK-SAME: explicit_paddings = [1, 2, 7, 8, 3, 4, 5, 6]
  // CHECK-SAME: padding = "EXPLICIT"
  // CHECK-SAME: strides = [5, 8, 6, 7]
  // CHECK-SAME: (tensor<1x3x32x32xf32>, tensor<4xi32>, tensor<1x8x32x32xf32>)

    // CHECK-SAME: device = "/job:localhost/replica:0/task:0/device:TPU:0"
    %1:2 = tf_executor.Enter %arg0 frame "frame" : tensor<i64>
    tf_executor.fetch %1#0 : tensor<i64>
  }
  func.return %0 : tensor<i64>
}

// CHECK-LABEL: func @testExitOp
// CHECK-SAME: ({{%.+}}: tensor<i64> {tf.device = "/job:localhost/replica:0/task:0/device:TPU:0"})

// CHECK-SAME: value = dense<0> : tensor<3x2xi32>
// CHECK-DAG: %[[LHS_DILATION:.*]] = "tf.Const"()
// CHECK-SAME: value = dense<[4, 1, 1]> : tensor<3xi32>
// CHECK-DAG: %[[LHS_DILATION_COPY:.*]] = "tf.Const"()
// CHECK-SAME: value = dense<[4, 1, 1]> : tensor<3xi32>
// CHECK-DAG: %[[RHS_DILATION:.*]] = "tf.Const"()
// CHECK-SAME: value = dense<1> : tensor<3xi32>
// CHECK-DAG: %[[RHS_DILATION_COPY:.*]] = "tf.Const"()

# it is being invoked.
# CHECK: module
# CHECK-SAME: tf.versions
# CHECK-SAME: bad_consumers
# CHECK-SAME: min_consumer
# CHECK-SAME: producer

# CHECK: tf_saved_model.session_initializer
# CHECK-SAME: initializers = [@[[restore:.*]]]

# CHECK: "tf_saved_model.asset"()
# CHECK-SAME: <{filename = [[filename:.*]], sym_name = "[[sym_name:.*]]"}> : () -> ()

# itself is verified in the common graphdef converter, so here just assert
# it is being invoked.
# CHECK: module
# CHECK-SAME: tf.versions
# CHECK-SAME: bad_consumers
# CHECK-SAME: min_consumer
# CHECK-SAME: producer

# CHECK: "tf_saved_model.global_tensor"()
# CHECK: "tf_saved_model.session_initializer"() <{initializers = [@[[INIT_FUNC:[a-zA-Z_0-9]+]]]}> : () -> ()

// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu6_fn_1}>
// CHECK-SAME: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[PARTITIONEDCALL_1:.*]] = "tf.PartitionedCall"(%arg0, %arg1, %[[CONST_0]])
// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu_fn_1}
// CHECK: %[[PARTITIONEDCALL_2:.*]] = "tf.PartitionedCall"(%arg0, %arg1, %[[CONST_0]])
// CHECK-SAME: f = @composite_conv2d_with_bias_fn_1}

// CHECK:      tf_device.replicate
// CHECK-SAME: [%[[OP_A]], %[[OP_B]], %[[OP_C]]] as %{{[a-z0-9]*}}: tensor<*xi1>
// CHECK-SAME: [%[[OP_D]], %[[OP_E]], %[[OP_F]]] as %{{[a-z0-9]*}}: tensor<*xi32>
// CHECK-SAME: [%[[OP_G]], %[[OP_H]], %[[OP_I]]] as %{{[a-z0-9]*}}: tensor<*xf32>
// CHECK-SAME: %[[OP_J]] as %{{[a-z0-9]*}}: tensor<*xi8>
// CHECK-SAME: %[[OP_K]] as %{{[a-z0-9]*}}: tensor<*xi16>
// CHECK-SAME: %[[OP_L]] as %{{[a-z0-9]*}}: tensor<*xi64>

  // CHECK-SAME: %arg0,
  // CHECK-SAME: %[[DQ_1]], %[[DQ_2]], %[[DQ_3]], %[[DQ_4]],
  // CHECK-SAME: %[[DQ_5]], %[[DQ_6]], %[[DQ_7]], %[[DQ_8]],
  // CHECK-SAME: %[[NONE]], %[[NONE]], %[[NONE]],
  // CHECK-SAME: %[[DQ_9]], %[[DQ_10]], %[[DQ_9]], %[[DQ_9]],
  // CHECK-SAME: %[[NONE]], %[[NONE]],
  // CHECK-SAME: %[[DQ_11]], %[[DQ_12]],
  // CHECK-SAME: %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]]) <{