def test_layers_view():
    X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    L = np.array([[10, 11, 12], [13, 14, 15], [16, 17, 18]])
    real_adata = ad.AnnData(X)
    real_adata.layers["L"] = L
    view_adata = real_adata[1:, 1:]
    real_hash = joblib.hash(real_adata)
    view_hash = joblib.hash(view_adata)

    assert view_adata.is_view

    with pytest.raises(ValueError):
        view_adata.layers["L2"] = L + 2

    assert view_adata.is_view  # Failing to set layer item makes adata not view
    assert real_hash == joblib.hash(real_adata)
    assert view_hash == joblib.hash(view_adata)

    view_adata.layers["L2"] = L[1:, 1:] + 2

    assert not view_adata.is_view
    assert real_hash == joblib.hash(real_adata)
    assert view_hash != joblib.hash(view_adata)
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)