def get(url, session=None, silent=not debug, **kwargs):
    """Retrieve a given URL and log response.

    :param session: a :class:`requests.Session` object.
    :param silent: if **True**, response status and URL will not be printed.
    """
    session = session or requests
    kwargs["verify"] = kwargs.get("verify", True)
    r = session.get(url, **kwargs)
    if not silent:
        status_code = click.style(
            str(r.status_code),
            fg="green" if r.status_code in (200, 304) else "red")
        click.echo(status_code + " " + url)
        if r.status_code == 404:
            raise PackageNotFoundError
    return r
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