=== "Python 3.10+"

    You can use the same builtin types as generics (with square brackets and types inside):

    * `list`
    * `tuple`
    * `set`
    * `dict`

    And the same as with Python 3.8, from the `typing` module:

    * `Union`
    * `Optional` (the same as with Python 3.8)
    * ...and others.

  // CHECK: %[[RESULT:.*]] = "tf.StridedSlice"(%arg0, %[[CST]], %[[CST0]], %[[CST1]])
  // CHECK-SAME: begin_mask = 7 : i64
  // CHECK-SAME: ellipsis_mask = 0 : i64
  // CHECK-SAME: end_mask = 14 : i64
  // CHECK-SAME: new_axis_mask = 0 : i64
  // CHECK-SAME: shrink_axis_mask = 0 : i64

// CHECK: %[[tconv:.*]] = "tfl.transpose_conv"(%arg1, %[[dq_w:.*]], %arg0, %[[b:.*]]) <{
// CHECK-NOT: asymmetric_quantize_inputs = true
// CHECK-SAME: padding = "SAME"
// CHECK: return %[[tconv:.*]]

// PerTensor-DAG: %[[w:.*]] = arith.constant dense<1.270000e+02> : tensor<1x32x42x128xf32>
// PerTensor-DAG: %[[b:.*]]= arith.constant dense<0.000000e+00> : tensor<1x32x42x128xf32>

    return %0 : tensor<2x3x1x3xf32>
  }
}

// -----

// Tests if reduce_window op following quantized same-scale op is quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1722 : i32}, tf_saved_model.semantics} {
  // CHECK-LABEL: main_00
  // CHECK-SAME: %[[ARG0:.*]]: tensor<2x3x1x1024xf32>

func.func @testHashTableAndInitializeTableToV2(%arg0: tensor<!tf_type.string>) {
  // CHECK: [[handle:%.*]] = "tf.HashTableV2"()
  // CHECK-SAME: container = ""
  // CHECK-SAME: key_dtype = !tf_type.string
  // CHECK-SAME: shared_name = "table"
  // CHECK-SAME: value_dtype = i32
  // CHECK-SAME: device = ""
  // CHECK-SAME: () -> tensor<!tf_type.resource>

    def "toString() generally produces same output as original exception"() {
        def original = new Exception("original exception")
        def placeholder = new PlaceholderException(original.getClass().name, original.message, null, original.toString(), null, original.cause)

        expect:
        placeholder.toString() == original.toString()
    }

type T struct {
	a, b, c int
	E
}

type E struct{}

func (T) b()  {} // ERROR "field and method named b|redeclares struct field name|field and method with the same name b"
func (*T) E() {} // ERROR "field and method named E|redeclares struct field name|field and method with the same name E"

func _() {
	var x T
	_ = x.a
	_ = x.b // no follow-on error here
	x.b()   // no follow-on error here
	_ = x.c

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 293 : i32}} {

// CHECK-LABEL: func @gather
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg0:%.*]]: !tfrt_fallback.tf_tensor, [[arg1:%.*]]: !tfrt_fallback.tf_tensor)
// CHECK: [[const:%.*]] = tfrt_fallback_async.const_dense_tensor

  %0 = "tfl.conv_2d"(%arg0, %arg1, %arg2) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>, tensor<128xf32>)...

    {!> ../../../docs_src/response_model/tutorial002.py!}
    ```

Now, whenever a browser is creating a user with a password, the API will return the same password in the response.

In this case, it might not be a problem, because it's the same user sending the password.

But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

!!! danger