TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  input_tensors->reserve(ninputs);
  for (int i = 0; i < ninputs; ++i) {
    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing input ", i, " into function '", fn_name,
        "'");

    # Create and save a simple model that involves a hash table.
    inputs, outputs = self._create_and_save_vocab_table_lookup_qat_model_tf1(
        self._input_saved_model_path, tags, signature_def_key
    )

    # Make sure that the desired input key and output key is present.
    self.assertIn('input_vocabs', inputs.keys())
    self.assertIn('lookup', outputs.keys())

  SmallVector<StringRef> inputs;
  mlir::dyn_cast_or_null<StringAttr>(entry_function_attr.get("inputs"))
      .strref()
      .split(inputs, /*Separator=*/",");

  return inputs;
}

void ConvertMainArgAttrs(func::FuncOp main_func_op, const int arg_idx,
                         const StringRef index_path) {
  const ArrayRef<NamedAttribute> arg_attrs =

          GetCompsiteFunctionName(op);
      if (!composite_function_name.has_value()) return failure();

      // Quantize inputs of quantizable composite functions.
      for (OpOperand &input : op->getOpOperands()) {
        Type element_type = getElementTypeOrSelf(input.get().getType());
        // Non-float cases won't be calibrated.
        if (!element_type.isF32()) {
          continue;
        }

	if len(a) != len(b) {
		panic("crypto/ecdh: internal error: mismatched isLess inputs")
	}

	// Copy the values into a fixed-size preallocated little-endian buffer.
	// 72 bytes is enough for every scalar in this package, and having a fixed
	// size lets us avoid heap allocations.
	if len(a) > 72 {
		panic("crypto/ecdh: internal error: isLess input too large")
	}
	bufA, bufB := make([]byte, 72), make([]byte, 72)

[[sec:task_inputs_outputs]]
== Task inputs and outputs

In the most common case, a task takes some inputs and generates some outputs.
We can consider the process of Java compilation as an example of a task.
The Java source files act as inputs of the task, while the generated class files, i.e. the result of the compilation, are the outputs of the task.

.Example task inputs and outputs
image::taskInputsOutputs.png[]

        visitProperties(task)

        then:
        _ * visitor.visitNested() >> true
        1 * visitor.visitInputProperty('bean', { it.call() == null }, false)
    }

    def "cycle in nested inputs is detected"() {
        def task = project.tasks.create("myTask", TaskWithNestedObject)
        def cycle = new Tree(value: "cycle", left: new Tree(value: "left"))
        cycle.right = cycle

        return false;
      }

      // Special case handling for UnidirectionalSequenceLSTMOp, which doesn't
      // support partial quantization of its inputs.
      // Below, we check all of the input constants for the
      // UnidirectionalSequenceLSTMOp to see if any of them would not be
      // quantized due to not meeting the minimum_elements_for_weights

  @SafeVarargs
  public static <T extends @Nullable Object> Iterable<T> concat(Iterable<? extends T>... inputs) {
    return FluentIterable.concat(inputs);
  }

  /**
   * Combines multiple iterables into a single iterable. The returned iterable has an iterator that
   * traverses the elements of each iterable in {@code inputs}. The input iterators are not polled
   * until necessary.
   *

    if (!filter_type || filter_type.getRank() != 4 ||
        !filter_type.hasStaticShape())
      return failure();

    Value input = tf_op.getInput();
    RankedTensorType input_type =
        mlir::dyn_cast<RankedTensorType>(input.getType());
    // Only rank size four input will be only available by the tf.Conv2D
    // operator verification.
    if (!input_type || input_type.isDynamicDim(3)) {
      return failure();