%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>

// CHECK-SAME: (%[[A:.*]]: tensor<5x7xf32>, %[[B:.*]]: tensor<11x7xf32>)
func.func @matmul_transpose_b(%a: tensor<5x7xf32>, %b: tensor<11x7xf32>) -> tensor<5x11xf32> {
  // CHECK: %[[UPDATED_B:.*]] = "mhlo.transpose"(%[[B]]) <{permutation = dense<[1, 0]> : tensor<2xi64>}>
  // CHECK: "mhlo.dot"(%[[A]], %[[UPDATED_B]])
  %0 = "tf.MatMul"(%a, %b) {transpose_a = false, transpose_b = true} : (tensor<5x7xf32>, tensor<11x7xf32>) -> tensor<5x11xf32>

// CHECK:           }) : (tensor<5x10xf32>, tensor<3x1xi32>, tensor<10x3xf32>) -> tensor<5x10xf32>
// CHECK:           return %[[VAL_3]] : tensor<5x10xf32>
// CHECK:         }
func.func @convert_scatter_update_with_non_trailing_update_window_dims(
  %arg0: tensor<5x10xf32>,
  %arg1: tensor<3x1xi32>,
  %arg2: tensor<10x3xf32>) -> tensor<5x10xf32>
{
  %0 = "mhlo.scatter"(%arg0, %arg1, %arg2) ({