// CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<*x!tf_type.resource>, %[[ARG_1:[a-z0-9]*]]: tensor<*x!tf_type.resource>)
func.func @replicate_resource_var_arg_shape(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource>) {
  %0:6 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<*x!tf_type.resource>) {n = 2: i32} {
    %1 = "tf.ReadVariableOp"(%ri) {dtype = "tfdtype$DT_FLOAT"} : (tensor<*x!tf_type.resource>) -> tensor<*xf32>

  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,j->i"}: (tensor<4x5x6xf32>, tensor<5xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
// CHECK-LABEL: einsum_no_match
// CHECK: %[[v0:.*]] = "tf.Einsum"(%arg0, %arg1) <{equation = "ijk,j->i"}> {T = "tfdtype$DT_FLOAT"} : (tensor<4x5x6xf32>, tensor<5xf32>) -> tensor<4xf32>
// CHECK: return %[[v0]]
}

        attr_map = ""
      } : (tensor<*x!tf_type.qint8>, tensor<*x!tf_type.qint8>, tensor<*xf32>, tensor<*xi32>, tensor<*xf32>, tensor<*xi32>, tensor<*xf32>, tensor<*xi32>) -> tensor<*x!tf_type.qint32>
    func.return %conv_out : tensor<*x!tf_type.qint32>
  }

  // MatMul.
  func.func private @internal_matmul_fn(%input : tensor<*x!tf_type.qint8>, %filter : tensor<*x!tf_type.qint8>,

    tensor<1x9x9x9x!tf_type.qint8>
  %quant_filter = "tf.Cast"(%filter) {} : (tensor<3x3x9x10xi8>) ->
    tensor<3x3x9x10x!tf_type.qint8>
  %0 = "tf.UniformQuantizedConvolution"(
    %quant_input, %quant_filter, %input_scale, %input_zp,
    %filter_scale, %filter_zp, %accum_scale, %accum_zp
  ) {
    Tin = "tfdtype$DT_QINT8", Tout = "tfdtype$DT_QINT32",
    attr_map = "", batch_group_count = 1 : i64,

    tf_executor.fetch %0#2 : !tf_executor.control
  }
  func.return
}

// CHECK-LABEL: func @testNextIterationMissingSourceDevice
func.func @testNextIterationMissingSourceDevice() {
  tf_executor.graph {
    // CHECK:      tf_executor.NextIteration.Source
    %0:3 = tf_executor.NextIteration.Source : tensor<i64> {T = "tfdtype$DT_INT64"}

//   %cst_2 = "tf.Const"() {value = dense<[0.3, 0.4, 0.3, 0.4, 0.3, 0.4, 0.3, 0.4]> : tensor<8xf32>} : () -> tensor<8xf32>

func.func @testReadFileOpNotFolded() -> (tensor<!tf_type.string>) {
  %0 = "tf.Const"() { value = dense<"filepath"> : tensor<!tf_type.string> } : () -> tensor<!tf_type.string>
  // CHECK: %[[X:.*]] = "tf.ReadFile"
  %1 = "tf.ReadFile"(%0) : (tensor<!tf_type.string>) -> tensor<!tf_type.string>
  // CHECK: return %[[X]]
  func.return %1: tensor<!tf_type.string>

  %fq = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<f32>, tensor<f32>) -> tensor<3x3x3x16xf32>
  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
  func.return %rst : tensor<256x8x7x16xf32>

  %fq = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 5, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<f32>, tensor<f32>) -> tensor<3x3x3x16xf32>
  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
  func.return %rst : tensor<256x8x7x16xf32>

  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  func.return %0 : tensor<1x10x10x32xf32>
// CHECK: %[[BIASADD_0:.*]] = "tf.BiasAdd"(%arg0, %arg1) <{data_format = "NHWC"}> {T = "tfdtype$DT_FLOAT"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>