code: def forward(
      self,
      query,
      reference_points,
      input_flatten,
      input_spatial_shapes,
      input_level_start_index,
      input_padding_mask=None,
  ):
      n, len_q, _ = query.shape
      n, len_in, _ = input_flatten.shape
      assert (
          input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]
      ).sum() == len_in
  
      value = self.value_proj(input_flatten)
      if input_padding_mask is not None:
          value = paddle.where(
              input_padding_mask[..., None],
              paddle.zeros_like(value),
              value,
          )
      value = value.reshape(
          (n, len_in, self.num_heads, self.embed_dim // self.num_heads)
      )
      sampling_offsets = self.sampling_offsets(query).reshape(
          (
              n,
              len_q,
              self.num_heads,
              self.num_levels,
              self.num_points,
              2,
          )
      )
      attention_weights = self.attention_weights(query).reshape(
          (n, len_q, self.num_heads, self.num_levels * self.num_points)
      )
      attention_weights = F.softmax(attention_weights, -1).reshape(
          (n, len_q, self.num_heads, self.num_levels, self.num_points)
      )
      # n, len_q, num_heads, num_levels, num_points, 2
      if reference_points.shape[-1] == 2:
          offset_normalizer = paddle.stack(
              [
                  input_spatial_shapes[..., 1],
                  input_spatial_shapes[..., 0],
              ],
              -1,
          )
          sampling_locations = (
              reference_points[:, :, None, :, None, :]
              + sampling_offsets
              / offset_normalizer[None, None, None, :, None, :]
          )
      elif reference_points.shape[-1] == 4:
          sampling_locations = (
              reference_points[:, :, None, :, None, :2]
              + sampling_offsets
              / self.num_points
              * reference_points[:, :, None, :, None, 2:]
              * 0.5
          )
      else:
          raise ValueError(
              "Last dim of reference_points must be 2 or 4, but get {} instead.".format(
                  reference_points.shape[-1]
              )
          )
      if paddle.is_compiled_with_cuda():
          # GPU
          with use_custom_op("ms_deform_attn") as msda:
              output = msda.ms_deform_attn(
                  value,
                  input_spatial_shapes,
                  input_level_start_index,
                  sampling_locations,
                  attention_weights,
                  self.im2col_step,
              )
      else:
          # CPU
          output = slow_ms_deform_attn(
              value,
              input_spatial_shapes,
              sampling_locations,
              attention_weights,
          )
      output = self.output_proj(output)
      return output
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import param_init
  from paddlepanseg.models.param_init import THLinearInitMixin
  from paddlepanseg.utils import use_custom_op
label_desc: Use image node_id 3 for calling the MSDeformAttn obj's underlying member method code with example usage: obj.forward(query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask) and returns: output