// with the sign bit of f and the result in the same bit position.
// Float32bits(Float32frombits(x)) == x.
func Float32bits(f float32) uint32 { return *(*uint32)(unsafe.Pointer(&f)) }

// Float32frombits returns the floating-point number corresponding
// to the IEEE 754 binary representation b, with the sign bit of b
// and the result in the same bit position.
// Float32frombits(Float32bits(x)) == x.

func.func @set_stateless(%arg: tensor<i32>, %cond: tensor<i1>) -> (tensor<i32>, tensor<i32>) {
  // CHECK: tf.While
  // CHECK-SAME: is_stateless = true
  %0 = "tf.While"(%arg) { cond = @no_side_effect_cond, body = @no_side_effect_body, is_stateless = false} : (tensor<i32>) -> (tensor<i32>)
  // CHECK: tf.If
  // CHECK-SAME: is_stateless = true

// CHECK: %[[q2:.*]] = "quantfork.qcast"(%arg0)
// CHECK-SAME: tensor<1x3x4x3x!quant.uniform<i8:f32, 0.58754816990272674:-128>>
// CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])

// CHECK: %[[call:.*]] = "tf.PartitionedCall"(%[[dq2]], %[[dq1]], %[[dq0]])
// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu6_fn_10
// CHECK: %[[q3:.*]] = "quantfork.qcast"(%[[call]]) {volatile}
// CHECK-SAME: tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>

      CHECK-SAME        loc("tfr_gen_test.py":%[[add_line]]:12)
      CHECK-NEXT:     scf.yield %[[Add]] : !tfr.tensor
      CHECK-SAME        loc(unknown)
      CHECK-NEXT:   }
      CHECK-SAME        loc("tfr_gen_test.py":%[[for_line]]:2)
      CHECK-NEXT:   %{{.*}} = arith.constant true
      CHECK-SAME        loc(unknown)
      CHECK-NEXT:   tfr.return %[[for_stmt]] : !tfr.tensor
      CHECK-SAME        loc(unknown)

# Verify that we match correctly the input / output when they are scalar.

# CHECK-LABEL: func @main
# CHECK-SAME:  (%{{[a-z0-9]+}}: tensor<f32> {tf.device = "/device:CPU:0"})
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "out:1,out"

# CHECK:           tf.Relu
# CHECK:           %[[IDENTITY:[a-z_0-9]+]]:2, {{.*}} = tf_executor.island wraps "tf.IdentityN"

// CHECK: func.func @main(%[[ARG:.*]]: tensor<?x!tf_type.string>
// CHECK-SAME: tf_saved_model.index_path = ["serving_default_input_vocabs:0"]
// CHECK-SAME: -> (tensor<*xi64> {tf_saved_model.index_path = ["StatefulPartitionedCall:0"]})
// CHECK-SAME: tf.entry_function = {inputs = "serving_default_input_vocabs:0", outputs = "StatefulPartitionedCall:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["main"]

   *       the same.
   *   <li>The unreserved characters ".", "-", "~", and "_" remain the same.
   *   <li>The general delimiters "@" and ":" remain the same.
   *   <li>The subdelimiters "!", "$", "&amp;", "'", "(", ")", "*", "+", ",", ";", and "=" remain
   *       the same.
   *   <li>The space character " " is converted into %20.

    // CHECK:      metadata
    // CHECK-SAME: args
    // CHECK-SAME: dtype: DT_INT32
    // CHECK-SAME: shape
    // CHECK-SAME: dim
    // CHECK-SAME: size: 8
    // CHECK-SAME: kind: PARAMETER
    // CHECK-SAME: sharding
    // CHECK-SAME: type: MAXIMAL
    // CHECK-SAME: tile_assignment_dimensions: 1
    // CHECK-SAME: tile_assignment_devices: 0
    // CHECK-SAME: retvals
    // CHECK-SAME: sharding

# Cookie Parameters

You can define Cookie parameters the same way you define `Query` and `Path` parameters.

## Import `Cookie`

First import `Cookie`:

=== "Python 3.10+"

    ```Python hl_lines="3"
    {!> ../../../docs_src/cookie_params/tutorial001_an_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="3"
    {!> ../../../docs_src/cookie_params/tutorial001_an_py39.py!}
    ```

=== "Python 3.8+"

  }
}

// CHECK-LABEL: quantize_dot_general_fn
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2xf32>
// CHECK: %[[CST:.+]] = stablehlo.constant dense<3.000000e-01> : tensor<2x3xf32>
// CHECK: %[[Q:.+]] = "quantfork.qcast"(%[[CST]]) : (tensor<2x3xf32>) -> tensor<2x3x!quant.uniform<i8:f32, 6.000000e-03>>
// CHECK: %[[CALL:.+]] = call @quantized_dot_general_fn(%[[ARG0]], %[[Q]])