code: def test_tile_grad(self, M, N, tiles, gc, dc):
      X = np.random.rand(M, N).astype(np.float32)
      axis = 1
  
      op = core.CreateOperator(
          "Tile",
          ["X"],
          "out",
          tiles=tiles,
          axis=axis,
      )
  
      def tile_ref(X, tiles, axis):
          dims = np.asarray([1, 1], dtype=int)
          dims[axis] = tiles
          tiled_data = np.tile(X, dims)
          return (tiled_data,)
  
      # Check against numpy reference
      self.assertReferenceChecks(gc, op, [X, tiles, axis], tile_ref)
      # Check over multiple devices
      self.assertDeviceChecks(dc, op, [X], [0])
  
      # Gradient check wrt X
      grad_op = core.CreateOperator(
          "TileGradient",
          ["dOut"],
          "dX",
          tiles=tiles,
          axis=axis,
      )
      dX = np.random.rand(M, N * tiles).astype(np.float32)
      self.assertDeviceChecks(dc, grad_op, [dX], [0])
  
file_path: pytorch/caffe2/python/operator_test/tile_op_test.py
imports: import numpy
  from hypothesis import given, settings
  import hypothesis.strategies
  import unittest
  from caffe2.python import core, workspace
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
label_desc: Use image node_id 2 for calling the TestTile obj's underlying member method code with example usage: obj.test_tile_grad(M, N, tiles, gc, dc) and returns: tiled_data