@parameterized.named_parameters(
      ('quantize', True, 0),
      ('not_quantize', False, 10000),
  )
  @test_util.run_in_graph_and_eager_modes
  def test_minimum_elements_for_weights(self, quantize, num_elements):
    self._create_matmul_model(
        input_shape=(1, 1024),
        weight_shape=(1024, 3),
        saved_model_path=self._input_saved_model_path,
    )

    tags = {tag_constants.SERVING}

        // CHECK: tf.TensorListStack{{.*}}: (tensor<!tf_type.variant<tensor<2x2xf32>>>, tensor<i32>) -> tensor<?x2x2xf32>
        %9 = "tf.TensorListStack"(%8, %7) {device = "", num_elements = -1 : i64} : (tensor<*x!tf_type.variant>, tensor<i32>) -> tensor<*xf32>
        tf_executor.yield
      }
      tf_executor.fetch
    }
    func.return
  }

}

// -----

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>'}}

   */

  ANY(ZERO, ONE, SEVERAL);

  private final Set<Feature<? super Collection>> implied;
  private final @Nullable Integer numElements;

  CollectionSize(int numElements) {
    this.implied = Collections.emptySet();
    this.numElements = numElements;
  }

  CollectionSize(Feature<? super Collection>... implied) {
    // Keep the order here, so that PerCollectionSizeTestSuiteBuilder

   */

  ANY(ZERO, ONE, SEVERAL);

  private final Set<Feature<? super Collection>> implied;
  private final @Nullable Integer numElements;

  CollectionSize(int numElements) {
    this.implied = Collections.emptySet();
    this.numElements = numElements;
  }

  CollectionSize(Feature<? super Collection>... implied) {
    // Keep the order here, so that PerCollectionSizeTestSuiteBuilder

    int added = 0;
    while (added < numElements) {
      // we could rely solely on #poll, but #drainTo might be more efficient when there are multiple
      // elements already available (e.g. LinkedBlockingQueue#drainTo locks only once)
      added += q.drainTo(buffer, numElements - added);
      if (added < numElements) { // not enough elements immediately available; will have to poll

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

  // Check that output_tensor == expected_tensor
  EXPECT_EQ(output_tensor->Type(), expected_tensor->Type());
  EXPECT_EQ(output_tensor->NumElements(), expected_tensor->NumElements());
  testing::CheckBufferDataIsEqual(
      output_tensor->Type(), output_tensor->NumElements(),
      output_tensor->Data(), expected_tensor->Data());
}

// Test against combinations of SavedVariables of
// 1. Varying dtypes

  EXPECT_EQ(revived_tensor->Type(), expected.dtype());
  EXPECT_EQ(revived_tensor->NumElements(), expected.NumElements());
  EXPECT_EQ(revived_tensor->NumDims(), expected.dims());
  for (int i = 0; i < expected.dims(); ++i) {
    EXPECT_EQ(revived_tensor->Dim(i), expected.dim_size(i));
  }

  testing::CheckBufferDataIsEqual(expected.dtype(), expected.NumElements(),
                                  revived_tensor->Data(), expected.data());
}

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