def test_view_of_view_modification():
    adata = ad.AnnData(np.zeros((10, 10)))
    adata[0, :][:, 5:].X = np.ones(5)
    assert np.all(adata.X[0, 5:] == np.ones(5))
    adata[[1, 2], :][:, [1, 2]].X = np.ones((2, 2))
    assert np.all(adata.X[1:3, 1:3] == np.ones((2, 2)))

    adata.X = sparse.csr_matrix(adata.X)
    adata[0, :][:, 5:].X = np.ones(5) * 2
    assert np.all(asarray(adata.X)[0, 5:] == np.ones(5) * 2)
    adata[[1, 2], :][:, [1, 2]].X = np.ones((2, 2)) * 2
    assert np.all(asarray(adata.X)[1:3, 1:3] == np.ones((2, 2)) * 2)
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