// zero reserved elements
      %num_elements = "tf.Const"() <{value = dense<0> : tensor<i32>}> {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> tensor<i32>

      %list = "tf.TensorListReserve"(%elem_shape, %num_elements) : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<64x1xbf16>>>

    Type shape_dtype = getElementTypeOrSelf(op.getElementShape().getType());
    Value num_elements = operands[1];
    IntegerAttr attr;
    if (matchPattern(num_elements, m_Constant(&attr))) {
      return CreateI32SplatConst(op.getLoc(), rewriter, {1}, attr.getInt());
    }
    if (auto const_op = num_elements.getDefiningOp<TF::ConstOp>()) {
      return CreateI32SplatConst(op->getLoc(), rewriter, {1},

                                    builder->getBoolAttr(false));
}

// Creates a mhlo.rng_uniform op with `builder` to generate `num_elements`
// 32-bit integer numbers in the range of [`lower_limit`, `upper_limit`).
static mhlo::RngOp CreateRngUniform32(Location loc, int num_elements,
                                      int lower_limit, int upper_limit,
                                      OpBuilder *builder) {

}

// -----

func.func @testFullyConnectedWithBadInputShape(%arg0: tensor<2x2x11xf32>, %arg1: tensor<40x40xf32>, %arg2: none) -> tensor<40xf32> {
  // expected-error @+1 {{expect 'input' num_elements % 40 == 0, got input type 'tensor<2x2x11xf32>'}}

In the current implementation, the resulting operations are statically shaped,
which means it must be possible to infer a bound on the full shape of the
TensorList. That is, the `element_shape` and `num_elements` arguments to a
tensor list creation op are constant.

A tensor list creation op `tf.EmptyTensorList`/`tf.TensorListReserve` will be
turned in to a zero-initialized buffer, and the size is initialized to 0

input_handle: the input list
tensor: the gathered result
num_elements: optional. If not -1, the number of elements in the list.
  }];

  let arguments = (ins
    TF_VariantTensor:$input_handle,
    TF_Int32Tensor:$element_shape,

    DefaultValuedOptionalAttr<I64Attr, "-1">:$num_elements
  );

  let results = (outs
    TF_Tensor:$tensor
  );

	if err == nil || !errors.IsAlreadyExists(err) {
		t.Error("Expected already exists error")
	}
}

func TestNewCreateOptionsFromUpdateOptions(t *testing.T) {
	f := fuzz.New().NilChance(0.0).NumElements(1, 1)

	// The goal here is to trigger when any changes are made to either
	// CreateOptions or UpdateOptions types, so we can update the converter.
	for i := 0; i < 20; i++ {
		in := &metav1.UpdateOptions{}