// tf.Max when using quantized integer types.
func.func @max_qint(%arg0: tensor<4x8x!tf_type.qint8>) -> tensor<4x1x!tf_type.qint8> {
  %dimension = "tf.Const"() { value = dense<1> : tensor<1xi64> } : () -> tensor<1xi64>
  %0 = "tf.Max"(%arg0, %dimension) { keep_dims = true }: (tensor<4x8x!tf_type.qint8>, tensor<1xi64>) -> tensor<4x1x!tf_type.qint8>
  func.return %0 : tensor<4x1x!tf_type.qint8>
}

// -----

// CHECK-LABEL: func @max_dynamic

}

func.func @FusingbiasAdd(%arg0: tensor<1x10x10x32xf32>, %arg1: tensor<32xf32>) -> tensor<1x10x10x32xf32> {
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  %1 = "tf.BiasAdd"(%0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>

  %3 = "tf.Const"() {value = dense<"y (WhitespaceTokenize/RaggedConcat/RaggedNRows/Const:0) = "> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
  "tf.Assert"(%arg0, %0, %1, %2, %arg1, %3, %arg2) {device = "", summarize = 3 : i64} : (tensor<i1>, tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<i64>, tensor<!tf_type.string>, tensor<i64>) -> ()

/// offset applied to generate the range values. The output tensor needs to
/// have a static shape.
///
/// For example an op like the following:
///   %result = "tf.Range"(%start, %limit, %delta) {Tidx = "tfdtype$DT_FLOAT"}
///      : (tensor<f32>, tensor<f32>, tensor<f32>) -> tensor<5xf32>
///
/// Output would be:
///   %iota = "mhlo.iota"() {iota_dimension = 0 : i64} : () -> tensor<5xf32>