import tensorflow as tf

BLOCK_PARAMS = {
    "layers_in_block": 2,
    "activation": "relu",
    "units": [256, 256],
    "units_in_plevel_hidden_layers": [256, 128, 64, 32],
}

VALID_MODELS = ["baseline"]

LOSS_ARRAY = [
    tf.keras.losses.MeanSquaredError(), # 0
    tf.keras.losses.MeanSquaredError(), # 1
    tf.keras.losses.MeanSquaredError(), # 2
    tf.keras.losses.MeanSquaredError(), # 3
    tf.keras.losses.MeanSquaredError(), # 4
    tf.keras.losses.MeanSquaredError(), # 5
    tf.keras.losses.MeanSquaredError(), # 6
    tf.keras.losses.MeanSquaredError(), # 7
    tf.keras.losses.MeanSquaredError(), # 8
    tf.keras.losses.MeanSquaredError(), # 9
    tf.keras.losses.MeanSquaredError(), # 10
    tf.keras.losses.MeanSquaredError(), # 11
    tf.keras.losses.MeanSquaredError(), # 12
    tf.keras.losses.MeanAbsoluteError(), # 13
    tf.keras.losses.MeanAbsoluteError(), # 14
    tf.keras.losses.MeanAbsoluteError(), # 15
    tf.keras.losses.MeanAbsoluteError(), # 16
    tf.keras.losses.MeanAbsoluteError(), # 17
]

ecb92e559c841011bbe9d80d83a89046f5ded908
<mask token>

<mask token>
BLOCK_PARAMS = {'layers_in_block': 2, 'activation': 'relu', 'units': [256, 
    256], 'units_in_plevel_hidden_layers': [256, 128, 64, 32]}
VALID_MODELS = ['baseline']
LOSS_ARRAY = [tf.keras.losses.MeanSquaredError(), tf.keras.losses.
    MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf.keras.losses
    .MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf.keras.
    losses.MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf.keras
    .losses.MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf.
    keras.losses.MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf
    .keras.losses.MeanSquaredError(), tf.keras.losses.MeanSquaredError(),
    tf.keras.losses.MeanAbsoluteError(), tf.keras.losses.MeanAbsoluteError(
    ), tf.keras.losses.MeanAbsoluteError(), tf.keras.losses.
    MeanAbsoluteError(), tf.keras.losses.MeanAbsoluteError()]

import tensorflow as tf
BLOCK_PARAMS = {'layers_in_block': 2, 'activation': 'relu', 'units': [256, 
    256], 'units_in_plevel_hidden_layers': [256, 128, 64, 32]}
VALID_MODELS = ['baseline']
LOSS_ARRAY = [tf.keras.losses.MeanSquaredError(), tf.keras.losses.
    MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf.keras.losses
    .MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf.keras.
    losses.MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf.keras
    .losses.MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf.
    keras.losses.MeanSquaredError(), tf.keras.losses.MeanSquaredError(), tf
    .keras.losses.MeanSquaredError(), tf.keras.losses.MeanSquaredError(),
    tf.keras.losses.MeanAbsoluteError(), tf.keras.losses.MeanAbsoluteError(
    ), tf.keras.losses.MeanAbsoluteError(), tf.keras.losses.
    MeanAbsoluteError(), tf.keras.losses.MeanAbsoluteError()]

import tensorflow as tf

BLOCK_PARAMS = {
    "layers_in_block": 2,
    "activation": "relu",
    "units": [256, 256],
    "units_in_plevel_hidden_layers": [256, 128, 64, 32],
}

VALID_MODELS = ["baseline"]

LOSS_ARRAY = [
    tf.keras.losses.MeanSquaredError(), # 0
    tf.keras.losses.MeanSquaredError(), # 1
    tf.keras.losses.MeanSquaredError(), # 2
    tf.keras.losses.MeanSquaredError(), # 3
    tf.keras.losses.MeanSquaredError(), # 4
    tf.keras.losses.MeanSquaredError(), # 5
    tf.keras.losses.MeanSquaredError(), # 6
    tf.keras.losses.MeanSquaredError(), # 7
    tf.keras.losses.MeanSquaredError(), # 8
    tf.keras.losses.MeanSquaredError(), # 9
    tf.keras.losses.MeanSquaredError(), # 10
    tf.keras.losses.MeanSquaredError(), # 11
    tf.keras.losses.MeanSquaredError(), # 12
    tf.keras.losses.MeanAbsoluteError(), # 13
    tf.keras.losses.MeanAbsoluteError(), # 14
    tf.keras.losses.MeanAbsoluteError(), # 15
    tf.keras.losses.MeanAbsoluteError(), # 16
    tf.keras.losses.MeanAbsoluteError(), # 17
]
