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 fixed_padding(inputs, kernel_size, data_format):
  """Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or [batch,
      height_in, width_in, channels] depending on data_format.
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
      Should be a positive integer.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    A tensor with the same format as the input with the data either intact
    (if kernel_size == 1) or padded (if kernel_size > 1).
  """
  pad_total = kernel_size - 1
  pad_beg = pad_total // 2
  pad_end = pad_total - pad_beg

  if data_format == 'channels_first':
    padded_inputs = tf.pad(
        tensor=inputs,
        paddings=[[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
  else:
    padded_inputs = tf.pad(
        tensor=inputs,
        paddings=[[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
  return padded_inputs