%387 = "tf.StridedSlice"(%365, %6, %15, %16) {begin_mask = 0 : i64, device = "", ellipsis_mask = 0 : i64, end_mask = 1 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<2xi64>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<0xi64>
  %388 = "tf.ConcatV2"(%386, %387, %14) {device = ""} : (tensor<1xi64>, tensor<0xi64>, tensor<i32>) -> tensor<1xi64>

  // CHECK: mhlo.dynamic_pad {{.*}} : (tensor<?xf32>, tensor<f32>, tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<?xf32>
  %1 = "tf.PadV2"(%arg0, %arg2, %arg1) : (tensor<?xf32>, tensor<1x2xi64>, tensor<f32>) -> tensor<?xf32>
  func.return %1 : tensor<?xf32>
}

// CHECK-SAME:                                      %[[VAL_0:.*]]: tensor<10x8xi64>,
// CHECK-SAME:                                      %[[VAL_1:.*]]: tensor<10x8xi64>) -> tensor<10x8xi64> {
// CHECK:           %[[VAL_2:.*]] = "tf.Mod"(%[[VAL_0]], %[[VAL_1]]) : (tensor<10x8xi64>, tensor<10x8xi64>) -> tensor<10x8xi64>
// CHECK:           return %[[VAL_2]] : tensor<10x8xi64>
// CHECK:         }

///   %scaled = "mhlo.multiply"(%iota, %delta)
///       {broadcast_dimensions = dense<[]> : tensor<0xi64>} :
///       (tensor<5xf32>, tensor<f32>) -> tensor<5xf32>
///   %result = "mhlo.add"(%scaled, %offset)
///       {broadcast_dimensions = dense<[]> : tensor<0xi64>} :
///       (tensor<5xf32>, tensor<f32>) -> tensor<5xf32>
///

  // CHECK:  return %1 : tensor<*xi32>
}

// CHECK-LABEL: @broadcast_to_ui32
func.func @broadcast_to_ui32(%arg0: tensor<ui32>, %arg1: tensor<1xi64>) -> tensor<10xui32> {
  %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<ui32>, tensor<1xi64>) -> tensor<10xui32>
  return %0 : tensor<10xui32>
  // CHECK:  %cst = arith.constant dense<1> : tensor<10xui32>

* 🌐 Add Japanese translation for `docs/ja/docs/deployment/server-workers.md`. PR [#10064](https://github.com/tiangolo/fastapi/pull/10064) by [@tamtam-fitness](https://github.com/tamtam-fitness).