code: def flow_level(
      name,
      x,
      x_mask,
      depth,
      split_dims,
      prior,
      inverse,
      init,
      dtype,
      **kwargs,
  ):
      """One level of flow."""
      flow_step_fn = flow_step_glow
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          reversible_ops = []
          for step in np.arange(depth):
              reversible_ops += [
                  functools.partial(
                      flow_step_fn,
                      name="{}_step".format(step),
                      split_dims=split_dims,
                      init=init,
                      dtype=dtype,
                      **kwargs,
                  )
              ]
          if prior:
              reversible_ops += [
                  functools.partial(
                      coupling,
                      name="{}_prior".format(depth),
                      split_dim="c",
                      identity_first=True,
                      init=init,
                      **kwargs,
                  )
              ]
          if inverse:
              reversible_ops = reversible_ops[::-1]
  
          logabsdets = tf.constant(0.0, dtype=dtype)
          for reversible_op in reversible_ops:
              x, logabsdet = reversible_op(
                  x=x, x_mask=x_mask, inverse=inverse
              )
              logabsdets += logabsdet
          return x, logabsdets
  
file_path: tensor2tensor/tensor2tensor/layers/transformer_glow_layers.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import scipy
  from tensor2tensor.layers import common_layers
  import tensor2tensor.layers.transformer_glow_layers_ops
  import tensorflow.compat.v1
label_desc: Use image node_id 7 for calling a global function with example usage: flow_level(name, x, x_mask, depth, split_dims, prior, inverse, init, dtype) and returns: x,  logabsdets