/**
     * Gets the original input words.
     * Returns a defensive copy to prevent external modification.
     *
     * @return The original input words (defensive copy).
     */
    public String[] getInputs() {
        return inputs == null ? null : inputs.clone();
    }

    /**
     * Gets the input words as a newline-separated string.
     *
     * @return The input words as a 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,

        final Iterator<ImageReader> readers = ImageIO.getImageReaders(input);
        if (readers.hasNext()) {
            final ImageReader reader = readers.next();
            try {
                reader.setInput(input);
                final ImageReadParam param = reader.getDefaultReadParam();
                final int width = reader.getWidth(0);
                final int height = reader.getHeight(0);

    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,

                options.setSeparateOutputDirs(false);
                options.setBackends(singletonList("html5"));
            });

            // TODO: Break the paths assumed here
            TaskInputs inputs = task.getInputs();
            inputs.files(extension.getCssFiles())
                .withPropertyName("manual")
                .withPathSensitivity(PathSensitivity.RELATIVE);
            inputs.dir("src/main/resources")

}

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) {