@Override
    public void setDoInput(final boolean doInput) {
        this.connection.setDoInput(doInput);
        this.doInput = doInput;
    }

    @Override
    public boolean getDoInput() {
        return this.connection.getDoInput();
    }

    @Override
    public void setDoOutput(final boolean doOutput) {
        this.connection.setDoOutput(doOutput);
        this.doOutput = doOutput;
    }

        final EditBody body = new EditBody();
        body.id = entity.getId();
        body.dictId = dictId;
        body.input = entity.getInput();
        body.output = entity.getOutput();
        return body;
    }

     * Returns a defensive copy to prevent external modification.
     *
     * @return array of input sequences (defensive copy)
     */
    public String[] getInputs() {
        return inputs == null ? null : inputs.clone();
    }

    /**
     * Returns all input sequences joined with newline characters as a single string.

    if (input_shape.num_dims == InferenceContext::kUnknownRank) {
      c.SetInput(i, c.UnknownShape());
      continue;
    }
    dims.reserve(input_shape.num_dims);
    for (int j = 0; j < input_shape.num_dims; ++j) {
      dims.push_back(c.MakeDim(input_shape.dims[j]));
    }
    c.SetInput(i, c.MakeShape(dims));
  }

  // TODO(bgogul): Handle input_tensors_as_shapes.

            File expectedOutputFile = useNativeExpanded
                ? new File(projectArtifact.expandedDistDir, "elasticsearch-" + bwcVersion.get() + "-SNAPSHOT")
                : projectArtifact.distFile;
            c.getInputs().file(new File(project.getBuildDir(), "refspec"));
            if (useNativeExpanded) {
                c.getOutputs().dir(expectedOutputFile);
            } else {
                c.getOutputs().files(expectedOutputFile);

void NegHelper(TF_Operation* n, TF_Graph* graph, TF_Status* s, const char* name,
               TF_Operation** op) {
  TF_OperationDescription* desc = TF_NewOperation(graph, "Neg", name);
  TF_Output neg_input = {n, 0};
  TF_AddInput(desc, neg_input);
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_NE(*op, nullptr);
}

TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s,

    public Object convert(final TypeLiteral role, final String value) {
        if (value.trim().startsWith("<")) {
            try {
                final MXParser parser = new MXParser();
                parser.setInput(new StringReader(value));
                parser.nextTag();

                return parse(parser, role);
            } catch (final Exception e) {

  EXPECT_EQ(0, TF_OperationNumInputs(scalar));
  EXPECT_EQ(0, TF_OperationNumInputs(feed));
  ASSERT_EQ(1, TF_OperationNumInputs(neg));
  TF_Output neg_input = TF_OperationInput({neg, 0});
  EXPECT_EQ(scalar, neg_input.oper);
  EXPECT_EQ(0, neg_input.index);

  // Test that we can't see control edges involving the source and sink nodes.
  TF_Operation* control_ops[100];

}

extern int TFE_OpGetFlatInputCount(const TFE_Op* op, TF_Status* status) {
  return tensorflow::unwrap(op)->GetInputs().size();
}

extern TFE_TensorHandle* TFE_OpGetFlatInput(const TFE_Op* op, int index,
                                            TF_Status* status) {
  return tensorflow::wrap(tensorflow::unwrap(op)->GetInputs()[index]);
}

TF_AttrType TFE_OpGetAttrType(TFE_Op* op, const char* attr_name,

}

void TF_OperationAllInputs(TF_Operation* oper, TF_Output* inputs,
                           int max_inputs) {
  for (auto* edge : oper->node.in_edges()) {
    if (edge->dst_input() >= 0 && edge->dst_input() < max_inputs) {
      inputs[edge->dst_input()] = {ToOperation(edge->src()),
                                   edge->src_output()};
    }
  }
}

int TF_OperationOutputNumConsumers(TF_Output oper_out) {