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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 img2img_transformer_base():
 """Base params for local1d attention."""

  hparams = image_transformer2d_base()
  # learning related flags
  hparams.layer_preprocess_sequence = "n"
  hparams.layer_postprocess_sequence = "da"
  # This version seems to benefit from a higher learning rate.
  hparams.learning_rate = 0.2
  hparams.layer_prepostprocess_dropout = 0.1
  hparams.learning_rate_warmup_steps = 12000
  hparams.filter_size = 2048
  hparams.num_encoder_layers = 4
  hparams.num_decoder_layers = 8
  hparams.block_length = 256
  hparams.block_width = 256
  hparams.dec_attention_type = cia.AttentionType.LOCAL_1D
  hparams.block_raster_scan = False
  return hparams