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.

# 'go' version in the go.mod file to 1.16, without actually updating the
# requirements to match.

[short] skip

env MODFMT='{{with .Module}}{{.Path}} {{.Version}}{{end}}'


# This module selects the same versions in Go 1.16 and 1.17 for all modules
# that provide packages (or test dependencies of packages) imported by the
# main module. However, in Go 1.16 it selects a higher version of a

# 'go' version in the go.mod file to 1.16, without actually updating the
# requirements to match.

[short] skip

env MODFMT='{{with .Module}}{{.Path}} {{.Version}}{{end}}'


# For this module, Go 1.16 selects the same versions of all explicit dependencies
# as Go 1.17 does. However, Go 1.16 selects a higher version of an *implicit*
# dependency, imported by a test of one of the (external) imported packages.

    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>

// and the module including the TFLite version.

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32> {
^bb0(%arg0: tensor<3x2xi32>):
  // CHECK: module attributes
  // CHECK-SAME: tfl.description = "MLIR Converted."
  // CHECK-SAME: tfl.schema_version = 3 : i32

  // CHECK:          %{{.*}} = "tfl.pseudo_const"() <{value = dense<{{\[\[1, 2\], \[3, 4\], \[5, 6\]\]}}> : tensor<3x2xi32>}>

	}
	return to
}

// SimplePodServiceAndAllSpecial finds the first Pod deployment that has a sidecar and doesn't use a headless service and removes all
// other "regular" pods that aren't part of the same Service. Pods that are part of the same Service but are in a
// different cluster or revision will still be included.
// Example:
//   - The full set of apps is a, b, c, d, e, headless, naked, and vm.
//   - The plain-pods are a, b and c.

// CHECK-SAME: %[[none]], %[[input_10]], %[[input_11]], %[[none]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]],
// CHECK-SAME: %[[none]], %[[input_21]], %[[input_22]], %[[input_23]])
// CHECK-SAME: effective_hidden_scale_intermediate = tensor<!quant.uniform<i8:f32, 0.0039215686274509803:-1>>

}

// CHECK-LABEL: reduce_window_with_max
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x9x10x3x!quant.uniform<i8:f32, 3.000000e-01:-5>>
// CHECK-SAME: %[[ARG1:.*]]: tensor<!quant.uniform<i8:f32, 3.000000e-01:-5>>
// CHECK: %[[MAX_POOL:.*]] = "tfl.max_pool_2d"(%[[ARG0]])