// CHECK-NEXT:  return %0 : tensor<2x3x2xi32>
// CHECK-NEXT:}

func.func @rng_bit_generator(%arg0: tensor<2xui64>) -> (tensor<2xui64>, tensor<10x12xui32>) {
  %output_state, %output = "vhlo.rng_bit_generator_v1"(%arg0) <{rng_algorithm = #vhlo<rng_algorithm_v1 DEFAULT>}> : (tensor<2xui64>) -> (tensor<2xui64>, tensor<10x12xui32>)
  func.return %output_state, %output : tensor<2xui64>, tensor<10x12xui32>
}

        algorithm = mlir::vhlo::RngAlgorithmV1::DEFAULT;
    }
    auto attr =
        mlir::vhlo::RngAlgorithmV1Attr::get(builder.getContext(), algorithm);
    attributes.emplace_back(builder.getNamedAttr("rng_algorithm", attr));
    return;
  }
}

// Pull in FlatBuffer writers for TFLite generated using TableGen

        byte[] dataHmac = getHmac(dataChecksum, codeHmac);
        SecretKeySpec dataKey = new SecretKeySpec(dataHmac, KerberosConstants.RC4_ALGORITHM);

        cipher = Cipher.getInstance(KerberosConstants.RC4_ALGORITHM);
        cipher.init(Cipher.DECRYPT_MODE, dataKey);

        int plainDataLength = data.length - KerberosConstants.CHECKSUM_SIZE;

  update_window_dims: [long];
  inserted_window_dims: [long];
  scatter_dims_to_operand_dims: [long];
  index_vector_dim: long;
  unique_indices: bool;
  update_computation_subgraph_index: int;
}

enum RngAlgorithm : byte {
  // An algorithm auto-selected by the system according to device type.
  DEFAULT = 0,
  // The Philox algorithm, as described in paper
  // ['Parallel Random Numbers: As Easy as 1, 2, 3']