code: def lmx_base():
      """Transformer on languagemodel_lm1b32k_packed.  50M Params."""
      hparams = transformer.transformer_tpu()
      # sharing is counterproductive when underparameterized
      hparams.shared_embedding_and_softmax_weights = False
      # we judge by log-ppl, so label smoothing hurts.
      hparams.label_smoothing = 0.0
      # This makes the batch size on GPU the same as on TPU for a packed problem
      # with sequence length 256.
      # TODO(noam): fix the mess that is the data reading pipeline.
      hparams.max_length = 256
      # larger batch since we only have a decoder
      hparams.batch_size = 4096
      # save some memory so we can have a larger model
      hparams.activation_dtype = "bfloat16"
      return hparams
  
file_path: tensor2tensor/tensor2tensor/models/research/lm_experiments.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
label_desc: Use image node_id 1 for calling a global function with example usage: lmx_base() and returns: hparams