%1 = stablehlo.transpose %arg0, dims = [0, 3, 1, 2] : (tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32>
  %2 = stablehlo.add %1, %0 : tensor<1x4x3x3xf32>
  return %2 : tensor<1x4x3x3xf32>
}
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32>
// CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant

        then:
        def scripts = scriptDetails()
        scripts.size() == 3 // same script applied 3 times
        scripts.collect { it.className }.unique().size() == 1
        scripts.collect { it.classpath }.unique().size() == 1
        getCompileBuildFileOperationsCount() == 8 // classpath and body for each script
    }

    def "same script can be applied from identical init script, settings script and build script"() {

debug::
A daemon is considered compatible only if debug is set to the same value as requested (`true` or `false`).

enable assertions::
A daemon is considered compatible only if enable assertions are set to the same value as requested (`true` or `false`).

default character encoding::
A daemon is considered compatible only if the default character encoding is set to the same value as requested.

 *
 * <p><b>Warning:</b> instances of type {@code Multimap} may not implement {@link Object#equals} in
 * the way you expect. Multimaps containing the same key-value pairs, even in the same order, may or
 * may not be equal and may or may not have the same {@code hashCode}. The recommended subinterfaces
 * provide much stronger guarantees.
 *
 * <h3>Comparison to a map of collections</h3>
 *

			},
		},
	}
}

func (p *bestEffortPolicy) mergeTestCases(numaNodes []int) []policyMergeTestCase {
	return []policyMergeTestCase{
		{
			name: "Two providers, 2 hints each, same mask (some with different bits), same preferred",
			hp: []HintProvider{
				&mockHintProvider{
					map[string][]TopologyHint{
						"resource1": {
							{
								NUMANodeAffinity: NewTestBitMask(0, 1),

        expect:
        succeeds("resolve")
    }

    def "buildscript resolvable configuration and consumable configuration from same project live in same resolved component"() {
        buildFile << """
            buildscript {
                configurations {
                    conf {
                        canBeConsumed = false

public abstract class DiscreteDomain<C extends Comparable> {

  /**
   * Returns the discrete domain for values of type {@code Integer}.
   *
   * <p>This method always returns the same object. That object is serializable; deserializing it
   * results in the same object too.
   *
   * @since 14.0 (since 10.0 as {@code DiscreteDomains.integers()})
   */
  public static DiscreteDomain<Integer> integers() {
    return IntegerDomain.INSTANCE;

    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>

    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>

If the passwords don't match, we return the same error.

#### Password hashing

"Hashing" means: converting some content (a password in this case) into a sequence of bytes (just a string) that looks like gibberish.

Whenever you pass exactly the same content (exactly the same password) you get exactly the same gibberish.

But you cannot convert from the gibberish back to the password.