code: def generate_train_batch(self):
      selected_keys = np.random.choice(
          self.list_of_keys, self.batch_size, True, None
      )
      data = np.zeros(self.data_shape, dtype=np.float32)
      seg = np.zeros(self.seg_shape, dtype=np.float32)
      case_properties = []
      for j, i in enumerate(selected_keys):
          if self.get_do_oversample(j):
              force_fg = True
          else:
              force_fg = False
  
          if "properties" in self._data[i].keys():
              properties = self._data[i]["properties"]
          else:
              with open(self._data[i]["properties_file"], "rb") as f:
                  properties = pickle.load(f)
          case_properties.append(properties)
  
          if os.path.isfile(self._data[i]["data_file"][:-4] + ".npy"):
              case_all_data = np.load(
                  self._data[i]["data_file"][:-4] + ".npy",
                  self.memmap_mode,
              )
          else:
              case_all_data = np.load(self._data[i]["data_file"])[
                  "data"
              ]
  
          if self.has_prev_stage:
              if not os.path.isfile(
                  self._data[i]["seg_from_prev_stage_file"]
              ):
                  raise UserWarning(
                      "seg from prev stage missing: {}. please run single_fold_eval.py with --predict_next_stage first.".format(
                          self._data[i]["seg_from_prev_stage_file"]
                      )
                  )
              if os.path.isfile(
                  self._data[i]["seg_from_prev_stage_file"][:-4]
                  + ".npy"
              ):
                  segs_from_previous_stage = np.load(
                      self._data[i]["seg_from_prev_stage_file"][:-4]
                      + ".npy",
                      mmap_mode=self.memmap_mode,
                  )[None]
              else:
                  segs_from_previous_stage = np.load(
                      self._data[i]["seg_from_prev_stage_file"]
                  )["data"][None]
  
              seg_key = np.random.choice(
                  segs_from_previous_stage.shape[0]
              )
              seg_from_previous_stage = segs_from_previous_stage[
                  seg_key : seg_key + 1
              ]
              assert all(
                  [
                      i == j
                      for i, j in zip(
                          seg_from_previous_stage.shape[1:],
                          case_all_data.shape[1:],
                      )
                  ]
              ), (
                  "seg_from_previous_stage does not match the shape of case_all_data: %s vs %s"
                  % (
                      str(seg_from_previous_stage.shape[1:]),
                      str(case_all_data.shape[1:]),
                  )
              )
          else:
              seg_from_previous_stage = None
  
          need_to_pad = self.need_to_pad.copy()
          for d in range(3):
              if (
                  need_to_pad[d] + case_all_data.shape[d + 1]
                  < self.patch_size[d]
              ):
                  need_to_pad[d] = (
                      self.patch_size[d] - case_all_data.shape[d + 1]
                  )
  
          shape = case_all_data.shape[1:]
          lb_x = -need_to_pad[0] // 2
          ub_x = (
              shape[0]
              + need_to_pad[0] // 2
              + need_to_pad[0] % 2
              - self.patch_size[0]
          )
          lb_y = -need_to_pad[1] // 2
          ub_y = (
              shape[1]
              + need_to_pad[1] // 2
              + need_to_pad[1] % 2
              - self.patch_size[1]
          )
          lb_z = -need_to_pad[2] // 2
          ub_z = (
              shape[2]
              + need_to_pad[2] // 2
              + need_to_pad[2] % 2
              - self.patch_size[2]
          )
  
          if not force_fg:
              bbox_x_lb = np.random.randint(lb_x, ub_x + 1)
              bbox_y_lb = np.random.randint(lb_y, ub_y + 1)
              bbox_z_lb = np.random.randint(lb_z, ub_z + 1)
          else:
              if "class_locations" not in properties.keys():
                  raise RuntimeError(
                      "Please rerun the preprocessing with the newest version of nnU-Net!"
                  )
  
              foreground_classes = np.array(
                  [
                      i
                      for i in properties["class_locations"].keys()
                      if len(properties["class_locations"][i]) != 0
                  ]
              )
              foreground_classes = foreground_classes[
                  foreground_classes > 0
              ]
  
              if len(foreground_classes) == 0:
                  selected_class = None
                  voxels_of_that_class = None
                  print(
                      "case does not contain any foreground classes", i
                  )
              else:
                  selected_class = np.random.choice(foreground_classes)
                  voxels_of_that_class = properties["class_locations"][
                      selected_class
                  ]
  
              if voxels_of_that_class is not None:
                  selected_voxel = voxels_of_that_class[
                      np.random.choice(len(voxels_of_that_class))
                  ]
                  bbox_x_lb = max(
                      lb_x, selected_voxel[0] - self.patch_size[0] // 2
                  )
                  bbox_y_lb = max(
                      lb_y, selected_voxel[1] - self.patch_size[1] // 2
                  )
                  bbox_z_lb = max(
                      lb_z, selected_voxel[2] - self.patch_size[2] // 2
                  )
              else:
                  bbox_x_lb = np.random.randint(lb_x, ub_x + 1)
                  bbox_y_lb = np.random.randint(lb_y, ub_y + 1)
                  bbox_z_lb = np.random.randint(lb_z, ub_z + 1)
  
          bbox_x_ub = bbox_x_lb + self.patch_size[0]
          bbox_y_ub = bbox_y_lb + self.patch_size[1]
          bbox_z_ub = bbox_z_lb + self.patch_size[2]
  
          valid_bbox_x_lb = max(0, bbox_x_lb)
          valid_bbox_x_ub = min(shape[0], bbox_x_ub)
          valid_bbox_y_lb = max(0, bbox_y_lb)
          valid_bbox_y_ub = min(shape[1], bbox_y_ub)
          valid_bbox_z_lb = max(0, bbox_z_lb)
          valid_bbox_z_ub = min(shape[2], bbox_z_ub)
  
          case_all_data = np.copy(
              case_all_data[
                  :,
                  valid_bbox_x_lb:valid_bbox_x_ub,
                  valid_bbox_y_lb:valid_bbox_y_ub,
                  valid_bbox_z_lb:valid_bbox_z_ub,
              ]
          )
          if seg_from_previous_stage is not None:
              seg_from_previous_stage = seg_from_previous_stage[
                  :,
                  valid_bbox_x_lb:valid_bbox_x_ub,
                  valid_bbox_y_lb:valid_bbox_y_ub,
                  valid_bbox_z_lb:valid_bbox_z_ub,
              ]
  
          data[j] = np.pad(
              case_all_data[:-1],
              (
                  (0, 0),
                  (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)),
                  (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0)),
                  (-min(0, bbox_z_lb), max(bbox_z_ub - shape[2], 0)),
              ),
              self.pad_mode,
              **self.pad_kwargs_data,
          )
  
          seg[j, 0] = np.pad(
              case_all_data[-1:],
              (
                  (0, 0),
                  (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)),
                  (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0)),
                  (-min(0, bbox_z_lb), max(bbox_z_ub - shape[2], 0)),
              ),
              "constant",
              **{"constant_values": -1},
          )
          if seg_from_previous_stage is not None:
              seg[j, 1] = np.pad(
                  seg_from_previous_stage,
                  (
                      (0, 0),
                      (
                          -min(0, bbox_x_lb),
                          max(bbox_x_ub - shape[0], 0),
                      ),
                      (
                          -min(0, bbox_y_lb),
                          max(bbox_y_ub - shape[1], 0),
                      ),
                      (
                          -min(0, bbox_z_lb),
                          max(bbox_z_ub - shape[2], 0),
                      ),
                  ),
                  "constant",
                  **{"constant_values": 0},
              )
      return {
          "data": data,
          "seg": seg,
          "properties": case_properties,
          "keys": selected_keys,
      }
  
imports: import os
  import pickle
  import numpy
  from collections import OrderedDict
  from abc import abstractmethod
label_desc: Use image node_id 4 for calling the DataLoader3D obj's underlying member method code with example usage: obj.generate_train_batch() and returns: dict