*/
    String getName(T element);

    /**
     * Returns the original identity name of the given element which
     * may be used to uniquely identify the element if the name returned
     * by getName() is not unique in the hierarchy.
     *
     * @param element the element, cannot be null
     * @return the identity name of the element, never null
     */
    String getIdentityName(T element);

    /**

  auto ctrl_dep =
      NodeWith(Op("Identity"), Inputs(Out(0, switch_on_compilation_pred)));
  // Check that we pipe int32 inputs through an IdentityN to avoid extra D2H
  // copies.
  EXPECT_THAT(
      stateful_partitioned_call_op,
      NodeWith(Inputs(Out(NodeWith(Op("IdentityN"), CtrlDeps(ctrl_dep))))));
}
#endif

}  // namespace

    call_op_returns.append(call_op.getResults().begin(),
                           call_op.getResults().end());
    SetFunctionPrivate(func_op);
  }

  // Creates Identity/IdentityN ops for returing values. This allows us to
  // restore the same output tensor names in python.
  int32_t output_count = 0;
  // Map from node name to the list of the OutputInfos of its outputs that are

  //tensorflow/c/experimental/ops/gen:generate_cpp -- \
  --output_dir="${current_dir}" \
  --api_dirs="${api_dir}" \
  --source_dir=third_party/tensorflow"

${generate} \
  --category=array \
  Identity \
  IdentityN \
  ZerosLike \
  Shape \
  ExpandDims \
  OnesLike

${generate} \
  --category=math \
  Mul \
  Conj \
  AddV2 \
  MatMul \
  Neg \
  Sum \
  Sub \
  Div \
  DivNoNan \

# The GKE environments don't have kubelets with certificates that
# identify the system:nodes group.  They use the kubelet identity
# TODO: remove this once new nodes are granted individual identities and the
# NodeAuthorizer is enabled.
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: kubelet-cluster-admin
  labels:
    addonmanager.kubernetes.io/mode: Reconcile
roleRef:
  apiGroup: rbac.authorization.k8s.io

    ```mlir
    func @my_fn(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
      %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
      %identity_n:2 = "tf.IdentityN"(%arg1, %identity) : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)
      return %identity, %identity_n#0 : tensor<i32>, tensor<i32>
    }
    ```
  }];

    }

    /**
     * Determine the identity of the work unit that uniquely identifies it
     * among the other work units of the same type in the current build.
     */
    Identity identify(Map<String, ValueSnapshot> identityInputs, Map<String, CurrentFileCollectionFingerprint> identityFileInputs);

    interface Identity {
        /**
         * The identity of the work unit that uniquely identifies it

Laura Kassovic <******@****.***> 1701107622 -0800

Laura Kassovic <******@****.***> 1701107622 -0800

// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst              input
//  \       |             |                     |
//   \  (tf.Identity) (tf.Identity)   =>   quant.qcast
//    \     |             |                     |
//       tf.FakeQuantWithMinMaxVars        quant.dcast
//                   |                          |
//