case *ast.IndexListExpr:
		// Generic type with multiple parameters.
		if len(t.Indices) > 0 {
			var b strings.Builder
			b.WriteString(recvString(t.X))
			b.WriteByte('[')
			b.WriteString(recvParam(t.Indices[0]))
			for _, e := range t.Indices[1:] {
				b.WriteString(", ")
				b.WriteString(recvParam(e))
			}
			b.WriteByte(']')
			return b.String()
		}
	}

	case *ParenExpr:
		Walk(v, n.X)

	case *SelectorExpr:
		Walk(v, n.X)
		Walk(v, n.Sel)

	case *IndexExpr:
		Walk(v, n.X)
		Walk(v, n.Index)

	case *IndexListExpr:
		Walk(v, n.X)
		walkList(v, n.Indices)

	case *SliceExpr:
		Walk(v, n.X)
		if n.Low != nil {
			Walk(v, n.Low)
		}
		if n.High != nil {
			Walk(v, n.High)
		}
		if n.Max != nil {
			Walk(v, n.Max)
		}

    AtomicDoubleArray aa = new AtomicDoubleArray(0);
    assertEquals(0, aa.length());
    assertThrows(IndexOutOfBoundsException.class, () -> aa.get(0));
  }

  /** get and set for out of bound indices throw IndexOutOfBoundsException */
  public void testIndexing() {
    AtomicDoubleArray aa = new AtomicDoubleArray(SIZE);
    for (int index : new int[] {-1, SIZE}) {

  const MIN_WIDTH = 4;
  const MIN_TEXT_WIDTH = 16;
  const TEXT_MARGIN = 2;
  const FONT_SIZE = 12;
  const MIN_FONT_SIZE = 8;

  // Fields
  let pivots = [];          // Indices of currently selected data.Sources entries.
  let matches = new Set();  // Indices of sources that match search
  let elems = new Map();    // Mapping from source index to display elements
  let displayList = [];     // List of boxes to display.

  // CHECK:  %values, %indices = "tfl.topk_v2"(%arg0, %cst) : (tensor<1x4xf32>, tensor<i32>) -> (tensor<1x4xf32>, tensor<1x4xi32>)
  // CHECK:  return %values, %indices : tensor<1x4xf32>, tensor<1x4xi32>
}

func.func @approx_top_k_with_min_k(%arg0: tensor<1x4xf32>) -> (tensor<1x4xf32>, tensor<1x4xi32>) {

      Option<"enable_custom_op_quantization_",
              "enable-custom-op-quantization", "std::string", "",
              "Specifies which pairs of a custom op and indices are quantizable where the indices are separated with a space.">,
  ];
}

def PrepareTFPass : Pass<"tfl-prepare-tf", "mlir::func::FuncOp"> {
  let summary = "Prepare TF for legalization to TensorFlow Lite dialect.";

                             .Union();
      }

      auto indices = dim_metadata.getIndices();
      std::vector<int> vector_indices(indices.size(), 0);
      int max_of_indices = 0;
      for (int j = 0, end = indices.size(); j < end; j++) {
        vector_indices[j] = indices[j];
        if (vector_indices[j] > max_of_indices) {
          max_of_indices = vector_indices[j];

}

func (StatefulSetOrdinals) SwaggerDoc() map[string]string {
	return map_StatefulSetOrdinals
}

        }

        /**
         * This uses an optimized version of {@link String#regionMatches(int, String, int, int)}
         * which does not check for negative indices or integer overflow.
         */
        boolean isChildOfOrThis(String filePath, int offset) {
            if (prefix.isEmpty()) {
                return true;
            }

// Within this subspace, the first arch-specific opcode should be
// at offset A_ARCHSPECIFIC.
//
// Subspaces are aligned to a power of two so opcodes can be masked
// with AMask and used as compact array indices.
const (
	ABase386 = (1 + iota) << 11
	ABaseARM
	ABaseAMD64
	ABasePPC64
	ABaseARM64
	ABaseMIPS
	ABaseLoong64
	ABaseRISCV
	ABaseS390X
	ABaseWasm