code: def __init__(
      self,
      batch_size,
      image_size=112,
      num_classes=10000,
      placement=None,
      sbp=None,
  ):
      super().__init__()
  
      self.image_shape = (batch_size, 3, image_size, image_size)
      self.label_shape = (batch_size,)
      self.num_classes = num_classes
      self.placement = placement
      self.sbp = sbp
  
      if self.placement is not None and self.sbp is not None:
          self.image = flow.nn.Parameter(
              flow.randint(
                  0,
                  high=255,
                  size=self.image_shape,
                  dtype=flow.float32,
                  placement=self.placement,
                  sbp=self.sbp,
              ),
              requires_grad=False,
          )
          self.label = flow.nn.Parameter(
              flow.randint(
                  0,
                  high=self.num_classes,
                  size=self.label_shape,
                  placement=self.placement,
                  sbp=self.sbp,
              ).to(dtype=flow.int32),
              requires_grad=False,
          )
      else:
          self.image = flow.randint(
              0,
              high=255,
              size=self.image_shape,
              dtype=flow.float32,
              device="cuda",
          )
          self.label = flow.randint(
              0,
              high=self.num_classes,
              size=self.label_shape,
              device="cuda",
          ).to(dtype=flow.int32)
  
file_path: insightface/recognition/arcface_oneflow/utils/ofrecord_data_utils.py
imports: import oneflow
  import oneflow.nn
  import os
  from typing import List, Union
label_desc: Use image node_id 1 to create a new SyntheticDataLoader object from inherited base classes: flow.nn with example: obj = SyntheticDataLoader(batch_size, image_size, num_classes, placement, sbp)