docstr: Test with rois for multiple images in a batch
code: def test_bbox_transform(
      self,
      roi_counts,
      num_classes,
      rotated,
      angle_bound_on,
      clip_angle_thresh,
      gc,
      dc,
  ):
      """
      Test with rois for multiple images in a batch
      """
      rois, deltas, im_info = create_bbox_transform_inputs(
          roi_counts, num_classes, rotated
      )
  
      def bbox_transform_ref():
          ref_op = core.CreateOperator(
              "BBoxTransform",
              ["rois", "deltas", "im_info"],
              ["box_out"],
              apply_scale=False,
              rotated=rotated,
              angle_bound_on=angle_bound_on,
              clip_angle_thresh=clip_angle_thresh,
          )
          workspace.FeedBlob("rois", rois)
          workspace.FeedBlob("deltas", deltas)
          workspace.FeedBlob("im_info", im_info)
          workspace.RunOperatorOnce(ref_op)
          return workspace.FetchBlob("box_out")
  
      box_out = torch.tensor(bbox_transform_ref())
      a, b = torch.ops._caffe2.BBoxTransform(
          torch.tensor(rois),
          torch.tensor(deltas),
          torch.tensor(im_info),
          [1.0, 1.0, 1.0, 1.0],
          False,
          rotated,
          angle_bound_on,
          -90,
          90,
          clip_angle_thresh,
          legacy_plus_one=True,
      )
  
      assert_allclose(box_out, a)
  
imports: import struct
  import unittest
  import caffe2.python.hypothesis_test_util
  import hypothesis.strategies
  import numpy
  import torch
  from caffe2.python import core, workspace
  from hypothesis import given, settings
  from scipy.stats import norm
  from ._utils import assert_allclose
label_desc: Use image node_id 1 for calling the TorchIntegration obj's underlying member method code with example usage: obj.test_bbox_transform(roi_counts, num_classes, rotated, angle_bound_on, clip_angle_thresh, gc, dc) and returns: workspace