text: import torch_xla.core.xla_model as xm
  class Config(object):
      def __init__(self):
  
          # Learning Rates
          self.lr_backbone = 1e-5
          self.lr = 1e-4
  
          # Epochs
          self.epochs = 30
          self.lr_drop = 20
          self.start_epoch = 0
          self.weight_decay = 1e-4
  
          # Backbone
          self.backbone = 'resnet101'
          self.position_embedding = 'sine'
          self.dilation = True
          
          # Basic
          self.device = xm.xla_device()
          self.seed = 42
          self.batch_size = 32
          self.num_workers = 8
          self.checkpoint = './checkpoint.pth'
          self.clip_max_norm = 0.1
  
          # Transformer
          self.hidden_dim = 256
          self.pad_token_id = 0
          self.max_position_embeddings = 128
          self.layer_norm_eps = 1e-12
          self.dropout = 0.1
          self.vocab_size = 30522
  
          self.enc_layers = 6
          self.dec_layers = 6
          self.dim_feedforward = 2048
          self.nheads = 8
          self.pre_norm = True
  
          # Dataset
          self.dir = 'coco_dataset'
          self.limit = -1