def input = inputArtifact.get().asFile
                    def output = outputs.file(input.name + ".blue")
                    assert output.parentFile.directory && output.parentFile.list().length == 0
                    println "Transforming \${input.name} to \${output.name}"
                    println "Input exists: \${input.exists()}"

		}
	}
}

func TestNodeConditions(t *testing.T) {
	testCases := map[string]struct {
		inputs []evictionapi.Threshold
		result []v1.NodeConditionType
	}{
		"empty-list": {
			inputs: []evictionapi.Threshold{},
			result: []v1.NodeConditionType{},
		},
		"memory.available": {
			inputs: []evictionapi.Threshold{
				{Signal: evictionapi.SignalMemoryAvailable},
			},

}

// -----

func.func @testSplitNonConstSplitDim(%input: tensor<4x4xf32>, %split_dim: tensor<i32>) {
  %0:2 = "tf.Split"(%split_dim, %input) : (tensor<i32>, tensor<4x4xf32>) -> (tensor<*xf32>, tensor<*xf32>)
  func.return
}

func.func @testSplitUnknownRankSplitDim(%input: tensor<4x4xf32>, %split_dim: tensor<*xi32>) {
  %0:2 = "tf.Split"(%split_dim, %input) : (tensor<*xi32>, tensor<4x4xf32>) -> (tensor<*xf32>, tensor<*xf32>)

  return ExpandTo4DForConvImpl(a, true);
}

TypeAttr RescaleQtype(Type input, Attribute factor) {
  return quant::RescaleQuantizedType(input, factor);
}

// Returns `true` if reducing `axes` in `input` with `keep_dims=true` results in
// the specified `shape` and `false` otherwise.
static bool ShapeMatchesReduceWithKeepAxes(Value input,
                                           const mlir::Attribute &axes,

    Value input = op.getOperand();

    return rewriter.create<TFL::TransposeOp>(op.getLoc(), input, perm);
  }

  Value InsertExpandDimsOp(stablehlo::BroadcastInDimOp op,
                           PatternRewriter& rewriter, Value input,
                           int64_t output_rank) const {
    auto input_type = input.getType().cast<TensorType>();

  func.return %24 : tensor<1x4xf32>
}

// -----

// Coefficient inputs of LSTM op don't match the dimension with input operand `input_to_output_weights`.

                    def input = inputArtifact.get().asFile
                    def output = outputs.file(input.name + ".\${targetColor}")
                    assert output.parentFile.directory && output.parentFile.list().length == 0
                    println "Transforming \${input.name} to \${output.name}"
                    println "Input exists: \${input.exists()}"

  Value reshaped_input = builder.create<TF::ReshapeOp>(
      RankedTensorType::get(shape, element_type), input, shape_cst);
  return reshaped_input;
}

// Create a tensor which is equal to input[begin: begin + size].
Value BuildSliceOp(ImplicitLocOpBuilder& builder,
                   ConversionPatternRewriter& rewriter, Value input,
                   Value begin, ArrayRef<int64_t> shape, Type idx_type,

    func.return %0 : tensor<*xf32>
  }

  // Verifies handling of cases involving multiple iteration of feeding inputs.
  // CHECK-LABEL: @const_input_required
  func.func @const_input_required(%arg0: tensor<10xf64>) -> tensor<?xf64> attributes {tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2,_arg3", outputs = "_retval0"}} {
    %cst = "tf.Const"() {value = dense<6> : tensor<1xi32>} : () -> tensor<1xi32>

    protected A doBackward(B b) {
      return convert(bimap.inverse(), b);
    }

    private static <X, Y> Y convert(BiMap<X, Y> bimap, X input) {
      Y output = bimap.get(input);
      checkArgument(output != null, "No non-null mapping present for input: %s", input);
      return output;
    }

    @Override
    public boolean equals(@CheckForNull Object object) {