state.operands.reserve(num_operands + 1);
  for (int input = 0, e = num_operands + 1; input != e; ++input) {
    if (input < dst_input) {
      state.operands.push_back(dst->getOperand(input));
    } else if (input == dst_input) {
      state.operands.push_back(source->getResult(0));
    } else {
      state.operands.push_back(dst->getOperand(input - 1));
    }
  }

		gp2flags1flags = regInfo{inputs: []regMask{gp, gp, 0}, outputs: []regMask{gp, 0}}

		gp2flags     = regInfo{inputs: []regMask{gpsp, gpsp}}
		gp1flags     = regInfo{inputs: []regMask{gpsp}}
		gp0flagsLoad = regInfo{inputs: []regMask{gpspsbg, 0}}
		gp1flagsLoad = regInfo{inputs: []regMask{gpspsbg, gpsp, 0}}
		gp2flagsLoad = regInfo{inputs: []regMask{gpspsbg, gpsp, gpsp, 0}}
		flagsgp      = regInfo{inputs: nil, outputs: gponly}

    }

    void assertHasImmediateCancel(
        ListenableFuture<String> a, ListenableFuture<String> b, Exception e) {
      ImmutableSet<ListenableFuture<String>> inputs = ImmutableSet.of(a, b);
      smartAssertTrue(inputs, e, intersect(inputs, ImmutableSet.of(doneCancelled)));
    }
  }

  /**

    }

    void assertHasImmediateCancel(
        ListenableFuture<String> a, ListenableFuture<String> b, Exception e) {
      ImmutableSet<ListenableFuture<String>> inputs = ImmutableSet.of(a, b);
      smartAssertTrue(inputs, e, intersect(inputs, ImmutableSet.of(doneCancelled)));
    }
  }

  /**

      for (ClosingFuture<?> input : inputs) {
        input.becomeSubsumedInto(closeables);
      }
    }

    /**
     * Returns a new {@code ClosingFuture} pipeline step derived from the inputs by applying a
     * combining function to their values. The function can use a {@link DeferredCloser} to capture
     * objects to be closed when the pipeline is done.
     *

      for (ClosingFuture<?> input : inputs) {
        input.becomeSubsumedInto(closeables);
      }
    }

    /**
     * Returns a new {@code ClosingFuture} pipeline step derived from the inputs by applying a
     * combining function to their values. The function can use a {@link DeferredCloser} to capture
     * objects to be closed when the pipeline is done.
     *

void TF_AddInput(TF_OperationDescription* desc, TF_Output input) {
  desc->node_builder.Input(&input.oper->node, input.index);
}

void TF_AddInputList(TF_OperationDescription* desc, const TF_Output* inputs,
                     int num_inputs) {
  std::vector<NodeBuilder::NodeOut> input_list;
  input_list.reserve(num_inputs);
  for (int i = 0; i < num_inputs; ++i) {
    input_list.emplace_back(&inputs[i].oper->node, inputs[i].index);
  }

using ::mlir::tf_saved_model::kTfSavedModelIndexPathAttr;

// Helper struct that wraps inputs/outputs of a single SignatureDef.
struct SignatureDefData {
  // Note, we are using maps here to make order deterministic
  // for easily testing only.

  // Inputs defined in the signature def mapped to tensor names.
  std::map<std::string, std::string> inputs;
  // Outputs defined in the signature def mapped to tensor names.

}

def MarkInputOutputAliasesPass : Pass<"tf-device-mark-input-output-aliases", "ModuleOp"> {
  let summary = "Marks device cluster inputs-output pairs that read/write to the same variable as aliases";

  let description = [{
    This pass analyzes the inputs and outputs to device cluster and marks those
    input-output pairs as aliases (using `tf.aliasing_output` attribute) which read

  let summary = opSummary # " operator";

  let description = [{
    Performs convolution operation on inputs.

    Inputs:
      `inputs[0]`: required: the input activation tensor
      `inputs[1]`: required: the filter weight tensor
      `inputs[2]`: optional: the bias tensor
  }];

  let results = (outs TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$output);

  let hasOptions = 0b1;
}