// CHECK-SAME:    sharding={
// CHECK-SAME:    {devices=[1,2]0,1}
// CHECK-SAME:    {maximal device=0}
// CHECK-SAME:    {replicated}
// CHECK-SAME:    }
// CHECK:         get-tuple-element((f32[128,10]{1,0}, f32[10,1024]{1,0}, f32[128,1024]{1,0}) %[[ARG_TUPLE]]), index=0
// CHECK-SAME:    sharding={devices=[1,2]0,1}

// CHECK: func @add(
// CHECK-SAME: {tf_saved_model.index_path = ["input1"]}
// CHECK-SAME: {tf_saved_model.index_path = ["input2"]}
// CHECK-SAME: {tf_saved_model.index_path = ["result"]}
// CHECK-SAME: tf.entry_function = {inputs = "input1:0,input2:0", outputs = "result:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["add"]

// CHECK: func @sub(
// CHECK-SAME: {tf_saved_model.index_path = ["input2"]}

  // CHECK: input_sharding_configuration
  // CHECK-SAME: ["\08\01\1A\01\01\22\01\00"]
  // CHECK: output_sharding_configuration
  // CHECK-SAME: ["\08\01\1A\01\01\22\01\00"]
  func.return
}

// CHECK-LABEL: func @func_without_sharding
// CHECK-SAME: (%{{[a-z0-9]+}}: tensor<*xi32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"})
// CHECK-SAME: -> (tensor<*xi32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"})

  // CHECK:      "mhlo.gather"({{.*}}) <{
  // CHECK-SAME:   dimension_numbers =
  // CHECK-SAME:     offset_dims = [1, 2]
  // CHECK-SAME:     collapsed_slice_dims = [0]
  // CHECK-SAME:     start_index_map = [0]
  // CHECK-SAME:     index_vector_dim = 1
  // CHECK-SAME:   indices_are_sorted = false
  // CHECK-SAME:   slice_sizes = dense<[1, 4, 128]>
  // CHECK-SAME: (tensor<2x4x128xf32>, tensor<2x1xi32>) -> tensor<2x4x128xf32>

# 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:.*]]]}> : () -> ()

# CHECK:      func @[[init]]
# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-NEXT: [[R6:%.*]] = "tf.Const"()

# CHECK-LABEL:      func @key(
# CHECK-SAME:   %[[ARG0:.*]]: tensor<3x5xf32> {tf_saved_model.index_path = ["y"]}
# CHECK-SAME:   %[[ARG1:.*]]: tensor<5x3xf32> {tf_saved_model.index_path = ["x"]}
# CHECK-SAME:                  tensor<3x3xf32> {tf_saved_model.index_path = ["t"]}
# CHECK-SAME:                  tensor<5x5xf32> {tf_saved_model.index_path = ["s"]}
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key"]

    // CHECK: %[[CALL:.+]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME: _entry_function = @composite_dot_general_fn_1
    // CHECK-SAME: _original_entry_function = "composite_dot_general_fn_1"
    // CHECK: return %[[CALL]]
  }

  // CHECK: @composite_dot_general_fn_1
  // CHECK-SAME: %[[ARG2:[^:[:space:]]+]]
  // CHECK-SAME: %[[ARG3:[^:[:space:]]+]]

  // CHECK: [[a:%.*]] = tf_mlrt.executeop([[input0]],
  // CHECK-SAME: AddV2
  // CHECK-SAME: op_key = 1
  // CHECK-NOT: __op_key
  %a = "tf.AddV2"(%input0, %const) {__op_key = 1: i32}: (tensor<i32>, tensor<i32>) -> tensor<i32>
  // CHECK: [[b:%.*]] = tf_mlrt.executeop([[a]])
  // CHECK-SAME: Abs
  // CHECK-SAME: op_key = 2
  // CHECK-NOT: __op_key

  // CHECK-SAME: quant.uniform<i8:f32, 0.023529411764705882:-128>
  // CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])
  // CHECK-SAME: quant.uniform<i8:f32, 0.023529411764705882:-128>
  // CHECK: %[[q3:.*]] = "quantfork.qcast"(%[[cst3]])
  // CHECK-SAME: quant.uniform<i8:f32, 3.9215686274509805E-9>
  // CHECK: %[[dq3:.*]] = "quantfork.dcast"(%[[q3]])
  // CHECK-SAME: quant.uniform<i8:f32, 3.9215686274509805E-9>

  // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1)
  // CHECK-SAME: data_format = "NHWC"
  // CHECK-SAME: dilations = [1, 3, 4, 2]
  // CHECK-SAME: explicit_paddings = [1, 2, 5, 6, 7, 8, 3, 4]
  // CHECK-SAME: padding = "EXPLICIT"
  // CHECK-SAME: strides = [5, 7, 8, 6]
  // CHECK-SAME: (tensor<1x32x32x3xf32>, tensor<1x1x3x8xf32>) -> tensor<1x7x6x8xf32>