def test_view_of_view(matrix_type, subset_func, subset_func2):
    adata = gen_adata((30, 15), X_type=matrix_type)
    adata.raw = adata
    if subset_func is single_subset:
        pytest.xfail("Other subset generating functions have trouble with this")
    var_s1 = subset_func(adata.var_names, min_size=4)
    var_view1 = adata[:, var_s1]
    var_s2 = subset_func2(var_view1.var_names)
    var_view2 = var_view1[:, var_s2]
    assert var_view2._adata_ref is adata
    obs_s1 = subset_func(adata.obs_names, min_size=4)
    obs_view1 = adata[obs_s1, :]
    obs_s2 = subset_func2(obs_view1.obs_names)
    assert adata[obs_s1, :][:, var_s1][obs_s2, :]._adata_ref is adata

    view_of_actual_copy = adata[:, var_s1].copy()[obs_s1, :].copy()[:, var_s2].copy()

    view_of_view_copy = adata[:, var_s1][obs_s1, :][:, var_s2].copy()

    assert_equal(view_of_actual_copy, view_of_view_copy, exact=True)
def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format,
                         name):
  """Strided 2-D convolution with explicit padding."""
  # The padding is consistent and is based only on `kernel_size`, not on the
  # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
  if strides > 1:
    inputs = fixed_padding(inputs, kernel_size, data_format)

  return tf.compat.v1.layers.conv2d(
      inputs=inputs,
      filters=filters,
      kernel_size=kernel_size,
      strides=strides,
      padding=('SAME' if strides == 1 else 'VALID'),
      use_bias=False,
      reuse=tf.AUTO_REUSE,
      kernel_initializer=tf.compat.v1.variance_scaling_initializer(),
      data_format=data_format,
      name=name)