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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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