code: def mtf_resnet_base():
    
    hparams = common_hparams.basic_params1()
    hparams.no_data_parallelism = True
    hparams.use_fixed_batch_size = True
    hparams.batch_size = 32
    hparams.max_length = 3072
    hparams.hidden_size = 256
    hparams.label_smoothing = 0.0
    # 8-way model-parallelism
    hparams.add_hparam("mesh_shape", "batch:8")
    hparams.add_hparam("layout", "batch:batch")
    hparams.add_hparam("filter_size", 1024)
  
    hparams.add_hparam("num_layers", 6)
    # Share weights between input and target embeddings
    hparams.shared_embedding = True
  
    hparams.shared_embedding_and_softmax_weights = True
    hparams.optimizer = "Adafactor"
    hparams.learning_rate_schedule = "rsqrt_decay"
    hparams.learning_rate_warmup_steps = 10000
    hparams.add_hparam("d_kv", 32)
  
    # Image related hparams
    hparams.add_hparam("img_len", 32)
    hparams.add_hparam("num_channels", 3)
    hparams.add_hparam("row_blocks", 1)
    hparams.add_hparam("col_blocks", 1)
    hparams.add_hparam("rows_size", 32)
    hparams.add_hparam("cols_size", 32)
  
    # Model-specific parameters
    hparams.add_hparam("layer_sizes", [3, 4, 6, 3])
    hparams.add_hparam("filter_sizes", [64, 64, 128, 256, 512])
    hparams.add_hparam("is_cifar", False)
  
    # Variable init
    hparams.initializer = "normal_unit_scaling"
    hparams.initializer_gain = 2.
  
    # TODO(nikip): Change optimization scheme?
    hparams.learning_rate = 0.1
    return hparams
docs: Set of hyperparameters.