code: def __init__(
      self,
      with_image_labels: bool = False,
      sync_caption_batch: bool = False,
      fp16: bool = False,
      roi_head_name: str = "",
      cap_batch_ratio: int = 4,
      with_caption: bool = False,
      dynamic_classifier: bool = False,
      **kwargs,
  ) -> None:
      super().__init__(**kwargs)
  
      self._entities = LVISV1Dataset.METAINFO["classes"]
      self._text_prompts = None
      # Turn on co-training with classification data
      self.with_image_labels = with_image_labels
      # Caption losses
      self.with_caption = with_caption
      # synchronize across GPUs to enlarge # "classes"
      self.sync_caption_batch = sync_caption_batch
      # Ratio between detection data and caption data
      self.cap_batch_ratio = cap_batch_ratio
      self.fp16 = fp16
      self.roi_head_name = roi_head_name
      # dynamic class sampling when training with 21K classes,
      # Federated loss is enabled when DYNAMIC_CLASSIFIER is on
      self.dynamic_classifier = dynamic_classifier
      self.return_proposal = False
      if self.dynamic_classifier:
          self.freq_weight = kwargs.pop("freq_weight")
          self.num_classes = kwargs.pop("num_classes")
          self.num_sample_cats = kwargs.pop("num_sample_cats")
  
imports: import copy
  from typing import List, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.logging import print_log
  from torch import Tensor
  from mmdet.datasets import LVISV1Dataset
  from mmdet.models.detectors.cascade_rcnn import CascadeRCNN
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
label_desc: Use image node_id 1 to create a new Detic object from inherited base classes: CascadeRCNN with example: obj = Detic(with_image_labels, sync_caption_batch, fp16, roi_head_name, cap_batch_ratio, with_caption, dynamic_classifier)