buildPod().phase(v1.PodSucceeded).index("0").Pod,
				buildPod().phase(v1.PodFailed).index("1").Pod,
				buildPod().phase(v1.PodSucceeded).trackingFinalizer().index("1").Pod,
				buildPod().phase(v1.PodSucceeded).trackingFinalizer().index("3").Pod,
				buildPod().uid("a").phase(v1.PodFailed).trackingFinalizer().index("2").Pod,
				buildPod().uid("b").phase(v1.PodFailed).trackingFinalizer().index("5").Pod,
			},
			wantRmFinalizers: 4,

  BufferOffset<tflite::Buffer> empty_buffer_;

  std::vector<BufferOffset<tflite::Buffer>> buffers_;
  // Maps subgraph index and tensor name in the graph to the tensor index.
  absl::flat_hash_map<int, absl::flat_hash_map<std::string, int>>
      tensor_index_map_;

  // Maps op name to index of the corresponding OperatorCode in opcodes_ vector.
  absl::flat_hash_map<std::string, uint32_t> opcode_index_map_;

type FunctionParam struct {
	Index int
}

func (fp *FunctionParam) print(ps *printState) {
	if fp.Index == 0 {
		ps.writeString("this")
	} else if ps.llvmStyle {
		if fp.Index == 1 {
			ps.writeString("fp")
		} else {
			fmt.Fprintf(&ps.buf, "fp%d", fp.Index-2)
		}
	} else {
		fmt.Fprintf(&ps.buf, "{parm#%d}", fp.Index)
	}
}

// and the latter will match any other path in the "/images/" subtree.
//
// As another example, consider the patterns "GET /" and "/index.html":
// both match a GET request for "/index.html", but the former pattern
// matches all other GET and HEAD requests, while the latter matches any
// request for "/index.html" that uses a different method.
// The patterns conflict.
//
// # Trailing-slash redirection
//

    }

    def "failure in transformation chain propagates (position in chain: #failingTransform)"() {
        given:

        Closure<String> possiblyFailingTransform = { index ->
            index == failingTransform ? "FailingDuplicator" : "Duplicator"
        }

  string customOption = ?;
}

class TFL_ConvOp<string mnemonic, string opSummary, int index,
                 list<Trait> additional_traits = []> :
    TFL_Op<mnemonic,[Pure,
                     AccumulatorUniformScale<2, 0, 1>,
                     AffineQuantizedOpInterface,
                     AffineOpCoefficient<index, 1>,
                     QuantizableResult,
                     TFL_SparseOp] # additional_traits> {

                                   OpResult op_result) {
    return ComputeOutputAsShape(op_result, &context);
  };

  // Return result element type at `index`.
  auto result_element_type_fn = [&](int index) {
    return mlir::cast<TensorType>(op->getResult(index).getType())
        .getElementType();
  };

  llvm::SmallVector<ShapedTypeComponents, 4> inferred_return_shapes;

   *     uniqueIndex(allColors, c -> c.toString());
   * assertThat(colorForName).containsEntry("red", red);
   * }</pre>
   *
   * <p>If your index may associate multiple values with each key, use {@link
   * Multimaps#index(Iterable, Function) Multimaps.index}.
   *
   * <p><b>Note:</b> on Java 8+, it is usually better to use streams. For example:
   *
   * <pre>{@code

				"size(self.array.filter(e, e%2 == 0)) == 3",
				"self.array.map(e, e * 20).filter(e, e > 50).exists(e, e == 60)",
				"size(self.array) == 8",
			},
			errors: map[string]string{
				"self.array[100] == 0": "index out of bounds: 100",
			},
		},
		{name: "listSet access",
			obj: map[string]interface{}{
				"set": []interface{}{1, 2, 3, 4, 5},
			},
			schema: objectTypePtr(map[string]schema.Structural{

			t.Errorf("%v: expected no error, but got %v", tc.name, err)
		}
	}
}

type predictableNameGenerator struct {
	index int
}

func (p *predictableNameGenerator) GenerateName(base string) string {
	p.index++
	return fmt.Sprintf("%s%d", base, p.index)
}

func TestStoreCreateGenerateNameConflict(t *testing.T) {