code: def predict_transform(prediction, inp_dim, anchors, num_classes):
      batch_size = prediction.shape[0]
      stride = inp_dim // prediction.shape[2]
      grid_size = inp_dim // stride
      bbox_attrs = 5 + num_classes
      num_anchors = len(anchors)
      prediction = prediction.reshape(
          shape=(
              batch_size,
              bbox_attrs * num_anchors,
              grid_size * grid_size,
          )
      )
      prediction = prediction.transpose(1, 2)
      prediction = prediction.reshape(
          shape=(
              batch_size,
              grid_size * grid_size * num_anchors,
              bbox_attrs,
          )
      )
      prediction_cpu = prediction.numpy()
      for i in (0, 1, 4):
          prediction_cpu[:, :, i] = 1 / (
              1 + np.exp(-prediction_cpu[:, :, i])
          )
      # Add the center offsets
      grid = np.arange(grid_size)
      a, b = np.meshgrid(grid, grid)
      x_offset = a.reshape((-1, 1))
      y_offset = b.reshape((-1, 1))
      x_y_offset = np.concatenate((x_offset, y_offset), 1)
      x_y_offset = np.tile(x_y_offset, (1, num_anchors))
      x_y_offset = x_y_offset.reshape((-1, 2))
      x_y_offset = np.expand_dims(x_y_offset, 0)
      anchors = [(a[0] / stride, a[1] / stride) for a in anchors]
      anchors = np.tile(anchors, (grid_size * grid_size, 1))
      anchors = np.expand_dims(anchors, 0)
      prediction_cpu[:, :, :2] += x_y_offset
      prediction_cpu[:, :, 2:4] = (
          np.exp(prediction_cpu[:, :, 2:4]) * anchors
      )
      prediction_cpu[:, :, 5 : 5 + num_classes] = 1 / (
          1 + np.exp(-prediction_cpu[:, :, 5 : 5 + num_classes])
      )
      prediction_cpu[:, :, :4] *= stride
      return Tensor(prediction_cpu)
  
imports: import sys
  import io
  import time
  import math
  import cv2
  import numpy
  from PIL import Image
  from tinygrad.tensor import Tensor
  from tinygrad.nn import BatchNorm2d, Conv2d
  from tinygrad.helpers import fetch
label_desc: Use image node_id 7 for calling a global function with example usage: predict_transform(prediction, inp_dim, anchors, num_classes) and returns: Tensor