def install_pip(self, package, save=True, save_dev=False):
        """Installs a pip package.

        :param save: if `True`, pins the package to the Hydrogen requirements
            YAML file.
        :param save_dev: if `True`, pins the package as a development
            dependency to the Hydrogen requirements YAML file.
        :param return: a **single** :class:`Requirement` object, representing
            the installed version of the given package.
        """
        requirement = Requirement.coerce(package)
        click.echo("pip install " + requirement.package)
        cmd = envoy.run("pip install {}".format(str(requirement)))
        if cmd.status_code == 0:
            installed_packages = get_installed_pypackages()
            package = installed_packages[requirement.package]
            requirement.version = "=={}".format(package.version)
            if save:
                self.requirements["all"].add(requirement)
            if save_dev:
                self.requirements["dev"].add(requirement)
            if save or save_dev:
                self.requirements.save()
            return requirement
        else:
            fatal(cmd.std_err)
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