// 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: 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"]

    // 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]])

// 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]])

			emon, epool, eimage := existingVolume.RBD.CephMonitors, existingVolume.RBD.RBDPool, existingVolume.RBD.RBDImage
			// two RBDs images are the same if they share the same Ceph monitor, are in the same RADOS Pool, and have the same image name
			// only one read-write mount is permitted for the same RBD image.
			// same RBD image mounted by multiple Pods conflicts unless all Pods mount the image read-only

	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 {

// CKECK-SAME: <{calibration_method = 5 : i32, id = "skipping_id", num_bins = 32 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32}> : (tensor<1x3x4x3xf32>) -> (tensor<1x3x4x3xf32>, tensor<f32>, tensor<f32>, tensor<512xi64>)
// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

  // CHECK: tfrt.while [[pred_0]]#1 @"while_body_add2/tfrt_body_10"
  // CHECK-SAME: parallel_iterations(10)
  // CHECK: [[pred_1:%.*]]:2 = tfrt.call @"while_cond_lt9/tfrt_predicate"
  // CHECK: tfrt.while [[pred_1]]#1 @"while_body_add2/tfrt_body_1"
  // CHECK-SAME: parallel_iterations(1)