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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 imagetransformer_ae_cifar():
 """Hyperparameters for CIFAR-10 experiments."""

  hparams = transformer_ae_small()
  hparams.filter_size = 512
  hparams.num_compress_steps = 3
  hparams.startup_steps = 10000
  hparams.is_2d = 0
  hparams.learning_rate_warmup_steps = 8000
  hparams.learning_rate = 0.2
  hparams.hidden_size = 512
  hparams.batch_size = 1
  hparams.max_length = 256
  hparams.dropout = 0.0
  hparams.clip_grad_norm = 0.  # i.e. no gradient clipping
  hparams.optimizer_adam_epsilon = 1e-9
  hparams.learning_rate_decay_scheme = "noam"
  hparams.learning_rate = 0.1
  hparams.initializer_gain = 0.2
  hparams.num_hidden_layers = 6
  hparams.initializer = "uniform_unit_scaling"
  hparams.weight_decay = 0.0
  hparams.optimizer_adam_beta1 = 0.9
  hparams.optimizer_adam_beta2 = 0.98
  hparams.label_smoothing = 0.0
  hparams.norm_type = "layer"
  hparams.layer_prepostprocess_dropout = 0.0
  hparams.num_heads = 8
  hparams.task = "image"
  hparams.ffn_layer = "conv_hidden_relu"
  # All hyperparameters ending in "dropout" are automatically set to 0.0
  # when not in training mode.
  hparams.attention_dropout = 0.0
  hparams.relu_dropout = 0.
  hparams.pos = "timing"  # timing, none
  hparams.nbr_decoder_problems = 1
  hparams.num_output_layers = 3
  # TODO(trandustin): semhash doesn't work if filter_size != hidden_size. For
  # now, set default to dvq.
  hparams.bottleneck_kind = "dvq"
  hparams.add_hparam("block_size", 1)

  # dilated attention based flags
  hparams.add_hparam("gap_sizes", [2, 4, 8, 16, 32, 64, 2, 4, 8, 16, 32, 64])
  hparams.add_hparam("dilated_attention", False)

  # image size related flags
  # assuming that the image has same height and width
  hparams.add_hparam("img_len", 32)
  hparams.add_hparam("num_channels", 3)
  # Local attention params
  hparams.add_hparam("local_and_global_att", False)
  hparams.add_hparam("block_length", 256)
  hparams.add_hparam("block_width", 128)
  hparams.num_encoder_layers = 4
  hparams.num_decoder_layers = 12
  hparams.add_hparam("dec_attention_type", cia.AttentionType.LOCAL_1D)
  hparams.add_hparam("block_raster_scan", False)
  hparams.add_hparam("shared_rel", False)

  # multipos attention params
  hparams.add_hparam("q_filter_width", 1)
  hparams.add_hparam("kv_filter_width", 1)

  hparams.add_hparam("unconditional", False)  # unconditional generation

  hparams.bottom["targets"] = modalities.image_channel_embeddings_bottom
  hparams.top["targets"] = modalities.image_channel_embeddings_top
  hparams.drop_inputs = True
  hparams.do_attend_compress = False
  hparams.do_attend_decompress = False
  return hparams