}

func.func @matmul_batch(%arg0: tensor<10x15xf32>, %arg1: tensor<15x17xf32>) -> tensor<10x17xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", device = "/device:CPU:0", name = "MatMul", adj_x = false, adj_y = false} :
(tensor<10x15xf32>, tensor<15x17xf32>) -> tensor<10x17xf32>
  func.return %0 : tensor<10x17xf32>
// CHECK-LABEL: matmul_batch

    func.func @main() -> (tensor<5x11xf32>) {
      %arg0 = "tf.Const"() {value = dense<-3.0> : tensor<5x7xf32>} : () -> tensor<5x7xf32>
      %arg1 = "tf.Const"() {value = dense<-3.0> : tensor<11x7xf32>} : () -> tensor<11x7xf32>

      %1 = "tf.MatMul"(%arg0, %arg1) {transpose_a = false, transpose_b = true} : (tensor<5x7xf32>, tensor<11x7xf32>) -> tensor<5x11xf32>

      func.return %1 : tensor<5x11xf32>
    }

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<10x19xf32>, %arg1: tensor<19x10xf32> {mhlo.is_same_data_across_replicas = true}) -> tensor<10x19xf32> {
    %0 = "tf.Shape"(%arg0) : (tensor<10x19xf32>) -> tensor<2xi64>
    %1 = "tf.Reshape"(%arg1, %0) : (tensor<19x10xf32>, tensor<2xi64>) -> tensor<10x19xf32>
    func.return %1 : tensor<10x19xf32>
  }
}

module attributes {tf.versions = {producer = 179 : i32}} {

  // CHECK-LABEL: func @main
  func.func @main(%arg0: tensor<10x19xf32>, %arg1: tensor<19x10xf32> {mhlo.is_same_data_across_replicas = true}) -> tensor<?xi64> {
    %0 = "tf.Shape"(%arg0) : (tensor<10x19xf32>) -> tensor<2xi64>
    %1 = "tf.Reshape"(%arg1, %0) : (tensor<19x10xf32>, tensor<2xi64>) -> tensor<?x?xf32>

    // CHECK: %[[RESULT:.*]] = mhlo.constant dense<[10, 19]>

func.func @testSoftmax(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 6400 : i64
  %0 = "tfl.softmax"(%arg0) {beta = 1.000000e+00 : f32} : (tensor<10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

func.func @testTanh(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 6400 : i64
  %0 = "tfl.tanh"(%arg0) : (tensor<10x10xf32>) -> tensor<10x10xf32>

// CHECK-LABEL: float_matmul
func.func @float_matmul(
  %arg0: tensor<1x10xf32>, %arg1: tensor<10x10xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %0 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = false
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

    %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
    %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.MatMul"(%arg0, %arg1) {
      transpose_a = false, transpose_b = false

// CHECK-DAG:       [[VAL_20:%.*]] = "tf.Const"() <{value = dense<-1> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK:           [[VAL_21:%.*]] = "tf.Reshape"([[VAL_15]]#0, [[VAL_20]]) : (tensor<1x10xf32>, tensor<1xi32>) -> tensor<10xf32>

  %0 = arith.constant dense<-1> : tensor<1xi32>
  %1 = "tfl.reduce_max"(%arg0, %0) {keep_dims = true} : (tensor<10x10xf32>, tensor<1xi32>) -> tensor<10x1xf32>
  %2 = tfl.sub(%arg0, %1) {fused_activation_function = "NONE"} : (tensor<10x10xf32>, tensor<10x1xf32>) -> tensor<10x10xf32>
  %3 = "tfl.exp"(%2) : (tensor<10x10xf32>) -> tensor<10x10xf32>

  "tfl.custom"(%0, %2) {custom_code = "FlexAssignVariableOp", custom_option = #tfl<const_bytes : "0x1041737369676E5661726961626C654F70003B121041737369676E5661726961626C654F701A001A002A0B0A056474797065120230012A140A0E76616C69646174655F736861706512022800320000024F3E1414042801">} : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> ()