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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 transformer_moe_2k():
 """Base transformers model with moe. Will have the following architecture: * No encoder. * Layer 0: a - sep (self-attention - unmasked separable convolutions) * Layer 1: a - sep * Layer 2: a - sep * Layer 3: a - sep * Layer 4: a - sep * Decoder architecture: * Layer 0: a - a - sepm (self-attention - enco/deco-attention - masked sep) * Layer 1: a - a - sepm * Layer 2: a - a - moe (mixture of expert layers in the middle) * Layer 3: a - a - sepm * Layer 4: a - a - sepm Returns: hparams """

  hparams = transformer_moe_8k()
  hparams.batch_size = 2048

  hparams.default_ff = "sep"

  # hparams.layer_types contains the network architecture:
  encoder_archi = "a/a/a/a/a"
  decoder_archi = "a-sepm/a-sepm/a-moe/a-sepm/a-sepm"
  hparams.layer_types = "{}#{}".format(encoder_archi, decoder_archi)

  return hparams