def test_view_mixin_copies_data(adata, array_type: type, attr):
    N = 100
    adata = ad.AnnData(
        obs=pd.DataFrame(index=np.arange(N)), var=pd.DataFrame(index=np.arange(N))
    )

    X = array_type(sparse.eye(N, N).multiply(np.arange(1, N + 1)))
    if attr == "X":
        adata.X = X
    else:
        getattr(adata, attr)["arr"] = X

    view = adata[:50]

    if attr == "X":
        arr_view = view.X
    else:
        arr_view = getattr(view, attr)["arr"]

    arr_view_copy = arr_view.copy()

    if sparse.issparse(X):
        assert not np.shares_memory(arr_view.indices, arr_view_copy.indices)
        assert not np.shares_memory(arr_view.indptr, arr_view_copy.indptr)
        assert not np.shares_memory(arr_view.data, arr_view_copy.data)

        arr_view_copy.data[0] = -5
        assert not np.array_equal(arr_view_copy.data, arr_view.data)
    else:
        assert not np.shares_memory(arr_view, arr_view_copy)

        arr_view_copy[0, 0] = -5
        assert not np.array_equal(arr_view_copy, arr_view)
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)