def load_pip_requirements(self, files_map=None, freeze=True):
        if files_map is None:
            files_map = self.default_pip_files
        for group, requirements_txt in files_map.items():
            path = Path(requirements_txt)
            if not path.exists() and group.lower() == "all" and freeze:
                cmd = envoy.run("pip freeze")
                self[group].loads(cmd.std_out)
            elif path.exists():
                self[group].load(path)
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)