def freeze(h, output_yaml, resolve, groups):
    """Output installed packages."""
    if not groups:
        groups = filter(lambda group: not group.lower().startswith("bower"),
                        h.requirements.keys())
    else:
        groups = [text_type.strip(group) for group in groups.split(",")]

    if output_yaml:
        for requirements in h.requirements.values():
            for requirement in requirements:
                if resolve and not requirement.version:
                    requirement.load_installed_version()
        click.echo(h.requirements.yaml)
    else:
        for group in groups:
            if not h.requirements[group]:
                continue
            click.echo("# {}".format(group))
            for requirement in h.requirements[group]:
                if resolve and not requirement.version:
                    requirement.load_installed_version()
                click.echo(str(requirement))
def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format,
                         name):
  """Strided 2-D convolution with explicit padding."""
  # The padding is consistent and is based only on `kernel_size`, not on the
  # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
  if strides > 1:
    inputs = fixed_padding(inputs, kernel_size, data_format)

  return tf.compat.v1.layers.conv2d(
      inputs=inputs,
      filters=filters,
      kernel_size=kernel_size,
      strides=strides,
      padding=('SAME' if strides == 1 else 'VALID'),
      use_bias=False,
      reuse=tf.AUTO_REUSE,
      kernel_initializer=tf.compat.v1.variance_scaling_initializer(),
      data_format=data_format,
      name=name)