// CHECK-DAG: %[[CONST:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<512x512xf32>}> : () -> tensor<512x512xf32>
// CHECK: %[[PARTITIONEDCALL:.*]] = "tf.PartitionedCall"(%arg0, %[[CONST]])
// CHECK-SAME: <{config = "",
// CHECK-SAME: f = @composite_matmul_fn_1}>
// CHECK-NOT: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[UNQUANTIZED_OUTPUT:.*]] = "tf.MatMul"(%arg0, %arg0)
// CHECK: }

// CHECK-LABEL: private @composite_matmul_fn_1

        id.getDisplayName() == "original -> current (foo)"
    }

    def "equals and hash code differentiate between same and different instances"() {
        def componentId = newComponentId("foo")

        when:
        def id = new TransformedComponentFileArtifactIdentifier(componentId, "a", "b")
        def same = new TransformedComponentFileArtifactIdentifier(componentId, "a", "b")

// RUN: odml-to-stablehlo-opt %s --transpose-commute-ops | FileCheck %s
// CHECK-LABEL:   func.func @commute_transpose_pad(
// CHECK-SAME:          %[[INPUT:.*]]: tensor<1x112x112x64xf32>,
// CHECK-SAME:          %[[PAD_VAL:.*]]: tensor<f32>) -> tensor<1x64x114x114xf32> {
// CHECK:           %[[PAD:.*]] = stablehlo.pad %[[INPUT]], %[[PAD_VAL]],
// CHECK:               low = [0, 1, 1, 0], high = [0, 1, 1, 0], interior = [0, 0, 0, 0]

// CHECK: tfrt_fallback_async.set_resource
// CHECK-SAME: {device = "/device:CPU:0", index = 0 : i64}


// CHECK-LABEL: func @init
// CHECK-SAME: {tfrt.cost_threshold = 1 : i64}
func.func @func_init() attributes {tf_saved_model.exported_names = ["init"]} {
  func.return
}

// CHECK-LABEL: func @basic
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg0:%.*]]: !tfrt_fallback.tf_tensor,

// 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.

# 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]])

	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"