import torch.nn as nn


def build_mlps(c_in, mlp_channels=None, ret_before_act=False, without_norm=False):
    layers = []
    num_layers = len(mlp_channels)

    for k in range(num_layers):
        if k + 1 == num_layers and ret_before_act:
            layers.append(nn.Linear(c_in, mlp_channels[k], bias=True))
        else:
            if without_norm:
                layers.extend([nn.Linear(c_in, mlp_channels[k], bias=True), nn.ReLU()]) 
            else:
                layers.extend([nn.Linear(c_in, mlp_channels[k], bias=False), nn.BatchNorm1d(mlp_channels[k]), nn.ReLU()])
            c_in = mlp_channels[k]

    return nn.Sequential(*layers)