// 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.

// 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>>

   *       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.

# 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"

# 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]])

  tf_executor.graph {
// CHECK: island
// CHECK: PartitionedCall
// CHECK-SAME: @_tpu_v1_compat_outlined::@_tpu_v1_compat_outlined_func0
    %1 = tf_executor.island {
     "tf.SomeTpuOp"() {_xla_compile_device_type = "TPU"} : () -> ()
      tf_executor.yield
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK: module
// CHECK-SAME: @_tpu_v1_compat_outlined

// CHECK-NEXT:   %[[RESULT_0:.*]] = "tf.While"(%[[ARG_0]])
// CHECK-SAME:   body = @while_body
// CHECK-SAME:   cond = @while_cond
// CHECK-SAME:   device = "/job:localhost/replica:0/task:0/device:CPU:0"
// CHECK-NEXT:   %[[RESULT_1:.*]] = tf_device.remote_run "/job:worker/replica:0/task:1" @[[MAIN_PARTITION_0:.*]](%[[ARG_1]])

	if cpid2 == ppid {
		t.Fatalf("Parent and child 2 have the same process ID")
	}

	if cpgrp2 == ppgrp {
		t.Fatalf("Parent and child 2 are in the same process group")
	}

	if cpid2 == cpgrp2 {
		t.Fatalf("Child 2's process group is its process ID")
	}

	if cpid1 == cpid2 {
		t.Fatalf("Child 1 and 2 have the same process ID")
	}

	if cpgrp1 != cpgrp2 {

 * The persistent ID of a potential build on disk.
 *
 * It is effectively the root dir of a build.
 * That is, two builds with the same root dir share the same workspace.
 *
 * In practice, this generally maps to what users would think of as “checkout” of a project.
 * Builds of the same checkout over time will share the same workspace ID.
 *
 * This ID is persisted in the root build's project cache dir.