# 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)

        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();

    func.return
  }
}

// -----

// CHECK-LABEL: module attributes
module attributes {tf_saved_model.semantics} {

  // Test case: Sanity check handling of non-bound inputs.
  // The pass shouldn't do anything in this case.

  // CHECK: func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})

// CHECK: "tf.LookupTableSizeV2"(%arg1)
// CHECK: "tf.LookupTableFindV2"(%arg1