code: def affine_transform(self, theta: torch.Tensor):
      # (spatial_dims, ...) -> (spatial_dims + 1, ...)
      grid_padded = torch.cat(
          [self.grid, torch.ones_like(self.grid[:1])]
      )
  
      # grid_warped[b,p,...] = sum_over_q(grid_padded[q,...] * theta[b,p,q]
      if self.spatial_dims == 2:
          grid_warped = torch.einsum(
              "qij,bpq->bpij", grid_padded, theta.reshape(-1, 2, 3)
          )
      elif self.spatial_dims == 3:
          grid_warped = torch.einsum(
              "qijk,bpq->bpijk", grid_padded, theta.reshape(-1, 3, 4)
          )
      else:
          raise ValueError(
              f"do not support spatial_dims={self.spatial_dims}"
          )
      return grid_warped
  
imports: from __future__ import annotations
  import torch
  from torch import nn
  from torch.nn import functional
  from monai.networks.blocks.regunet_block import RegistrationDownSampleBlock, RegistrationExtractionBlock, RegistrationResidualConvBlock, get_conv_block, get_deconv_block
  from monai.networks.utils import meshgrid_ij
label_desc: Use image node_id 3 for calling the AffineHead obj's underlying member method code with example usage: obj.affine_transform(theta) and returns: grid_warped