(data_streams=gin.REQUIRED, variable_shapes=True,
            batch_size_per_device=32, batch_size=None, eval_batch_size=32,
            bucket_length=32, buckets=None,
            buckets_include_inputs_in_length=False,
            batch_shuffle_size=None, max_eval_length=None,
            # TODO(afrozm): Unify padding logic.
            id_to_mask=None, strict_pad_on_len=False)
return Inputs(train_stream=batch_train_stream,
                eval_stream=batch_eval_stream,
                train_eval_stream=batch_train_eval_stream)
Batcher: create trax Inputs from single-example data-streams.
Batcher: create trax Inputs from single-example data-streams.
Batcher : create trax Inputs from single - example data - streams .
def batcher(data_streams=gin.REQUIRED, variable_shapes=True,
            batch_size_per_device=32, batch_size=None, eval_batch_size=32,
            bucket_length=32, buckets=None,
            buckets_include_inputs_in_length=False,
            batch_shuffle_size=None, max_eval_length=None,
            # TODO(afrozm): Unify padding logic.
            id_to_mask=None, strict_pad_on_len=False):
  """Batcher: create trax Inputs from single-example data-streams."""
  # TODO(lukaszkaiser, jonni): revisit arguments, their semantics and naming.
  # For now leaving the arguments as in batch_fn to reduce gin config changes.
  if callable(data_streams):  # If we pass a function, e.g., through gin, call.
    train_stream, eval_stream = data_streams()
  else:
    train_stream, eval_stream = data_streams
  # pylint: disable=g-long-lambda
  batch_train_stream = lambda n_devices: batch_fn(
      train_stream(), True, n_devices, variable_shapes,
      batch_size_per_device, batch_size, eval_batch_size,
      bucket_length, buckets, buckets_include_inputs_in_length,
      batch_shuffle_size, max_eval_length, id_to_mask, strict_pad_on_len)
  batch_eval_stream = lambda n_devices: batch_fn(
      eval_stream(), False, n_devices, variable_shapes,
      batch_size_per_device, batch_size, eval_batch_size,
      bucket_length, buckets, buckets_include_inputs_in_length,
      batch_shuffle_size, max_eval_length, id_to_mask, strict_pad_on_len)
  batch_train_eval_stream = lambda n_devices: batch_fn(
      train_stream(), False, n_devices, variable_shapes,
      batch_size_per_device, batch_size, eval_batch_size,
      bucket_length, buckets, buckets_include_inputs_in_length,
      batch_shuffle_size, max_eval_length, id_to_mask, strict_pad_on_len)
  # pylint: enable=g-long-lambda
  return Inputs(train_stream=batch_train_stream,
                eval_stream=batch_eval_stream,
                train_eval_stream=batch_train_eval_stream)
def batcher ( data_streams = gin . REQUIRED , variable_shapes = True , batch_size_per_device = 32 , batch_size = None , eval_batch_size = 32 , bucket_length = 32 , buckets = None , buckets_include_inputs_in_length = False , batch_shuffle_size = None , max_eval_length = None , # TODO(afrozm): Unify padding logic. id_to_mask = None , strict_pad_on_len = False ) : # TODO(lukaszkaiser, jonni): revisit arguments, their semantics and naming. # For now leaving the arguments as in batch_fn to reduce gin config changes. if callable ( data_streams ) : # If we pass a function, e.g., through gin, call. train_stream , eval_stream = data_streams ( ) else : train_stream , eval_stream = data_streams # pylint: disable=g-long-lambda batch_train_stream = lambda n_devices : batch_fn ( train_stream ( ) , True , n_devices , variable_shapes , batch_size_per_device , batch_size , eval_batch_size , bucket_length , buckets , buckets_include_inputs_in_length , batch_shuffle_size , max_eval_length , id_to_mask , strict_pad_on_len ) batch_eval_stream = lambda n_devices : batch_fn ( eval_stream ( ) , False , n_devices , variable_shapes , batch_size_per_device , batch_size , eval_batch_size , bucket_length , buckets , buckets_include_inputs_in_length , batch_shuffle_size , max_eval_length , id_to_mask , strict_pad_on_len ) batch_train_eval_stream = lambda n_devices : batch_fn ( train_stream ( ) , False , n_devices , variable_shapes , batch_size_per_device , batch_size , eval_batch_size , bucket_length , buckets , buckets_include_inputs_in_length , batch_shuffle_size , max_eval_length , id_to_mask , strict_pad_on_len ) # pylint: enable=g-long-lambda return Inputs ( train_stream = batch_train_stream , eval_stream = batch_eval_stream , train_eval_stream = batch_train_eval_stream )