def install_bower(self, package, save=True, save_dev=False):
        """Installs a bower 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 list of tuples, containing all installed package names
            and versions, including any dependencies.
        """
        requirement = Requirement.coerce(package)
        url = Bower.get_package_url(requirement.package)

        installed = []
        for name, _ in self.get_bower_package(url):
            installed.append(Requirement(name, requirement.version))

        for requirement in installed:
            if save:
                self.requirements["bower"].add(requirement, replace=True)
            if save_dev:
                self.requirements["bower-dev"].add(requirement, replace=True)
            success("installed {}".format(str(requirement)))
        if save or save_dev:
            self.requirements.save()
        return installed
def batch_norm(inputs, training, data_format, name=''):
  """Performs a batch normalization using a standard set of parameters."""
  # We set fused=True for a significant performance boost. See
  # https://www.tensorflow.org/performance/performance_guide#common_fused_ops
  return tf.compat.v1.layers.batch_normalization(
      inputs=inputs,
      axis=1 if data_format == 'channels_first' else 3,
      momentum=_BATCH_NORM_DECAY,
      epsilon=_BATCH_NORM_EPSILON,
      center=True,
      scale=True,
      training=training,
      fused=True,
      name=name)