for (int i = 0; i < inputs.size(); i++) {
      GraphTensor* t = dyn_cast<GraphTensor>(inputs[i]);
      if (!t) {
        return tensorflow::errors::InvalidArgument(
            "Unable to cast input to GraphTensor");
      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }

  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) {

    public void test_equals() {
        String[] inputs1 = { "a", "b" };
        String[] inputs2 = { "b", "a" }; // Different order
        String[] inputs3 = { "c", "d" };

        CharMappingItem item1 = new CharMappingItem(1L, inputs1, "output");
        CharMappingItem item2 = new CharMappingItem(2L, inputs2, "output");
        CharMappingItem item3 = new CharMappingItem(3L, inputs3, "output");

     *
     * @param id the unique identifier for this mapping item
     * @param inputs array of input character sequences that will be mapped to the output
     * @param output the output character sequence that inputs will be mapped to
     */
    public CharMappingItem(final long id, final String[] inputs, final String output) {
        this.id = id;
        this.inputs = inputs;

     * @param inputs  The input words.
     * @param outputs The output synonyms.
     */
    public SynonymItem(final long id, final String[] inputs, final String[] outputs) {
        this.id = id;
        this.inputs = inputs;
        this.outputs = outputs;

        if (id == 0) {
            // create
            newInputs = inputs;
            newOutputs = outputs;
        }
    }

        }
    }

    /**
     * Reloads the character mapping items from the provided input stream.
     * Parses mapping rules in the format: input1,input2,... => output
     *
     * @param updater the mapping updater to use for writing changes, or null for read-only reload
     * @param in the input stream to read the mapping data from
     */

  TFE_DeleteContextOptions(opts);

  TFE_TensorHandle* input1 = TestMatrixTensorHandle(ctx);
  TFE_TensorHandle* input2 = TestMatrixTensorHandle(ctx);
  TFE_Op* identityOp = TFE_NewOp(ctx, "IdentityN", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_TensorHandle* inputs[] = {input1, input2};
  TFE_OpAddInputList(identityOp, inputs, 2, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

	"strings"
	"text/scanner"

	"cmd/asm/internal/flags"
	"cmd/internal/objabi"
	"cmd/internal/src"
)

// Input is the main input: a stack of readers and some macro definitions.
// It also handles #include processing (by pushing onto the input stack)
// and parses and instantiates macro definitions.
type Input struct {
	Stack
	includes        []string
	beginningOfLine bool
	ifdefStack      []bool

        // Test characters that need special encoding
        String input1 = "http://example.com/文档"; // Unicode characters
        String result1 = crawlingInfoHelper.generateId(input1);
        assertNotNull(result1);
        assertEquals(128, result1.length());

        String input2 = "http://example.com/path with spaces";
        String result2 = crawlingInfoHelper.generateId(input2);
        assertNotNull(result2);

/**
 * A future whose value is derived from a collection of input futures.
 *
 * @param <InputT> the type of the individual inputs
 * @param <OutputT> the type of the output (i.e. this) future
 */
@GwtCompatible
@SuppressWarnings(
    // Whenever both tests are cheap and functional, it's faster to use &, | instead of &&, ||
    "ShortCircuitBoolean")
abstract class AggregateFuture<InputT extends @Nullable Object, OutputT extends @Nullable Object>