code: def predict_noise_from_start(self, x_t, t, x0):
      results = (
          extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
          - x0
      ) / extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
      return results
  
imports: import copy
  import math
  import random
  import warnings
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import build_activation_layer
  from mmcv.ops import batched_nms
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox2roi, bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, get_box_wh, scale_boxes
  from mmdet.utils import InstanceList
label_desc: Use image node_id 13 for calling the DynamicDiffusionDetHead obj's underlying member method code with example usage: obj.predict_noise_from_start(x_t, t, x0) and returns: results