(self, trax_layer, batch_size=None, initializer_rng=None,
               rng=None, rng_updater=None, dtype=None)
Creates a Keras layer wrapping around a Trax layer.

    Args:
      trax_layer: an object of class `trax.layers.Layer`, the trax layer to
        wrap.
      batch_size: (optional) an integer, the batch size that this Keras layer
        will be used on. Keras sometimes needs to generate a TF graph for a
        layer (e.g. for acceleration or checkpointing). The inputs used to trace
        the graph will have `None` as the length of their batch dimensions, so
        as to generate a graph that can handle any batch size. Some Trax layers
        can't handle tensors whose shapes contain `None`. If `batch_size` is set
        to an integer, the graph will be traced with `batch_size` as the batch
        size instead of `None`. Note that in this case the graph (and the Keras
        layer) can only be used on a specific batch size. If you want to use a
        different batch size, you need to create another `AsKeras` object
        with a different `batch_size`.
      initializer_rng: (optional) an RNG key used to create the weights and
        state if `trax_layer` doesn't have them. If `None`,
        `trax.fastmath.random.get_prng(0)` will be used.
      rng: (optional) an RNG key for the forward function (aka the "forward
        key"). If `None`, `trax.fastmath.random.get_prng(0)` will be used.
      rng_updater: (optional) a function of type rng_key -> rng_key, used to
        update the forward key after each forward pass. If `None`, the function
        `lambda x: trax.fastmath.random.split(x, 1)[0]` will be used, which
        advances the RNG key.
      dtype: (optional) the dtype of the inputs. See the `dtype` argument of
        `tf.keras.layers.Layer.__init__` for details.
Creates a Keras layer wrapping around a Trax layer.
Creates a Keras layer wrapping around a Trax layer .
def __init__(self, trax_layer, batch_size=None, initializer_rng=None,
               rng=None, rng_updater=None, dtype=None):
    """Creates a Keras layer wrapping around a Trax layer.

    Args:
      trax_layer: an object of class `trax.layers.Layer`, the trax layer to
        wrap.
      batch_size: (optional) an integer, the batch size that this Keras layer
        will be used on. Keras sometimes needs to generate a TF graph for a
        layer (e.g. for acceleration or checkpointing). The inputs used to trace
        the graph will have `None` as the length of their batch dimensions, so
        as to generate a graph that can handle any batch size. Some Trax layers
        can't handle tensors whose shapes contain `None`. If `batch_size` is set
        to an integer, the graph will be traced with `batch_size` as the batch
        size instead of `None`. Note that in this case the graph (and the Keras
        layer) can only be used on a specific batch size. If you want to use a
        different batch size, you need to create another `AsKeras` object
        with a different `batch_size`.
      initializer_rng: (optional) an RNG key used to create the weights and
        state if `trax_layer` doesn't have them. If `None`,
        `trax.fastmath.random.get_prng(0)` will be used.
      rng: (optional) an RNG key for the forward function (aka the "forward
        key"). If `None`, `trax.fastmath.random.get_prng(0)` will be used.
      rng_updater: (optional) a function of type rng_key -> rng_key, used to
        update the forward key after each forward pass. If `None`, the function
        `lambda x: trax.fastmath.random.split(x, 1)[0]` will be used, which
        advances the RNG key.
      dtype: (optional) the dtype of the inputs. See the `dtype` argument of
        `tf.keras.layers.Layer.__init__` for details.
    """
    super().__init__(dtype=dtype)
    with math_lib.use_backend(math_lib.Backend.TFNP):
      if initializer_rng is None:
        initializer_rng = math_lib.random.get_prng(0)
      if rng is None:
        rng = math_lib.random.get_prng(0)
      if rng_updater is None:
        rng_updater = lambda x: math_lib.random.split(x, 1)[0]
      self._trax_layer = trax_layer
      self._batch_size = batch_size
      self._initializer_rng = initializer_rng
      self._forward_rng_init = rng
      self._rng_updater = rng_updater
def __init__ ( self , trax_layer , batch_size = None , initializer_rng = None , rng = None , rng_updater = None , dtype = None ) : super ( ) . __init__ ( dtype = dtype ) with math_lib . use_backend ( math_lib . Backend . TFNP ) : if initializer_rng is None : initializer_rng = math_lib . random . get_prng ( 0 ) if rng is None : rng = math_lib . random . get_prng ( 0 ) if rng_updater is None : rng_updater = lambda x : math_lib . random . split ( x , 1 ) [ 0 ] self . _trax_layer = trax_layer self . _batch_size = batch_size self . _initializer_rng = initializer_rng self . _forward_rng_init = rng self . _rng_updater = rng_updater