code: def get_coco_config():
      """Returns the configuration for COCO detection using DeformableDETR."""
      config = ml_collections.ConfigDict()
      config.experiment_name = "coco_detection_deformable_detr"
  
      # Compiler
      config.compiler_config = ml_collections.ConfigDict()
      config.compiler_config.train_remat = True
      config.compiler_config.attention_batching_mode = "auto"
  
      # Dataset.
      config.dataset_name = "coco_deformable_detr_detection"
      config.dataset_configs = ml_collections.ConfigDict()
      config.dataset_configs.prefetch_to_device = 2
      config.dataset_configs.shuffle_buffer_size = 10_000
      config.dataset_configs.max_boxes = 299
      config.dataset_configs.max_size = 1333
      config.dataset_configs.valid_max_size = 1333
      config.data_dtype_str = "float32"
  
      # Model.
      config.model_dtype_str = "float32"
      config.model_name = "deformable_detr"
      config.matcher = "hungarian"
      config.num_classes = 91
      config.embed_dim = 256
      config.enc_embed_dim = 256
      config.num_queries = 300
      config.num_feature_levels = 4
      config.num_heads = 8
      config.num_encoder_layers = 6
      config.num_decoder_layers = 6
      config.transformer_ffn_dim = 1024
      config.num_enc_points = 4
      config.num_dec_points = 4
      config.backbone_num_filters = 64
      config.backbone_num_layers = 50
      config.dropout_rate = 0.1
  
      # Loss.
      config.aux_loss = True
      config.bbox_loss_coef = 5.0
      config.giou_loss_coef = 2.0
      config.class_loss_coef = 2.0
      config.focal_loss_alpha = 0.25
      config.focal_loss_gamma = 2.0
      # Use the mean num_boxes as normalization.
      config.normalization = "global"
  
      # Training.
      config.trainer_name = "deformable_detr_trainer"
      config.num_training_epochs = num_epochs
      config.rng_seed = 0
      config.batch_size = batch_size
      config.eval_batch_size = batch_size * 2
  
      # Optimization.
      config.optimizer_config = ml_collections.ConfigDict()
      config.optimizer_config.weight_decay = 1e-4
      config.optimizer_config.beta1 = 0.9
      config.optimizer_config.beta2 = 0.999
      config.optimizer_config.base_learning_rate = 2e-4
      config.optimizer_config.max_grad_norm = 0.1
      config.optimizer_config.learning_rate_decay_rate = 0.1
      config.optimizer_config.learning_rate_reduction = 0.1
      config.optimizer_config.learning_rate_decay_event = (
          num_epochs * 4 // 5 * steps_per_epoch
      )
  
      # Pretrained_backbone.
      config.load_pretrained_backbone = True
      config.freeze_backbone_batch_stats = True
      config.pretrained_backbone_configs = ml_collections.ConfigDict()
  
      # Logging.
      config.write_summary = True
      config.xprof = True  # Profile using xprof.
      config.log_summary_steps = 200  # Train summary steps.
      # Expensive summary operations freq.
      config.log_large_summary_steps = steps_per_epoch.identity()
      # Train steps before eval, typically one epoch.
      config.log_eval_steps = steps_per_epoch.identity()
      config.checkpoint = True  # Do checkpointing.
      config.checkpoint_steps = steps_per_epoch.identity()
      config.debug_train = False  # Debug mode during training.
      config.debug_eval = False  # Debug mode during eval.
  
      return config
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_coco_config() and returns: config