EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_DeleteStatus(s);
}

void CSession::SetInputs(
    std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs) {
  DeleteInputValues();
  inputs_.clear();
  for (const auto& p : inputs) {
    inputs_.emplace_back(TF_Output{p.first, 0});
    input_values_.emplace_back(p.second);
  }
}

void CSession::SetOutputs(std::initializer_list<TF_Operation*> outputs) {

      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, grad_inputs, s_, grad_outputs);
    } else {
      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, nullptr, s_, grad_outputs);
    }
  }

  void BuildErrorGraph(TF_Output* inputs, TF_Output* outputs) {

        "Unable to find the placeholder node as input to enqueue in the TF "
        "graph.");
    return;
  }

  VLOG(1) << "Running the enqueue op";
  TF_Output input{placeholder_op, 0};
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ &input, /*input_values*/ &tensor, /*ninputs*/ 1,
                // output related parameters

      final <T extends @Nullable Object, A extends @Nullable Object, R extends @Nullable Object>
          A result(Collector<T, A, R> collector, Iterable<T> inputs) {
        A accum = collector.supplier().get();
        for (T input : inputs) {
          collector.accumulator().accept(accum, input);
        }
        return accum;
      }
    },
    /** Get one accumulator for each element and merge the accumulators left-to-right. */

// checkPolicyCond returns a boolean to indicate if a condition is satisfied according
// to the passed operator
func checkPolicyCond(op string, input1, input2 string) bool {
	switch op {
	case policyCondEqual:
		return input1 == input2
	case policyCondStartsWith:
		return strings.HasPrefix(input1, input2)
	}
	return false
}

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
                     const char* name = "split3");

bool IsPlaceholder(const tensorflow::NodeDef& node_def);

          A result(Collector<T, A, R> collector, Iterable<T> inputs) {
        List<A> stack = new ArrayList<>();
        for (T input : inputs) {
          A newAccum = collector.supplier().get();
          collector.accumulator().accept(newAccum, input);
          push(stack, newAccum);
        }
        push(stack, collector.supplier().get());

		var err error
		inputs, err = filepathx.Glob(flag.Args()[0])
		fatalErr(err)
		if len(inputs) == 0 {
			fmt.Println("Usage: No input found")
		}
	default:
		flag.Usage()
		fatalIf(true, "Only 1 file can be decrypted")
		os.Exit(1)
	}
	for _, input := range inputs {
		processFile(input, privateKeys)
	}
}

func processFile(inputFileName string, privateKeys [][]byte) {

                    inputs = new String[inputStrings.size()];
                    for (int i = 0; i < inputs.length; i++) {
                        inputs[i] = unescape(inputStrings.get(i)).trim();
                    }

                    if (inputs.length > 0) {
                        id++;
                        final SynonymItem item = new SynonymItem(id, inputs, inputs);

      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.
     *