code: def get_temporal_dims_merged_into_space(
      num_frames: int, view_configs: Sequence[ml_collections.ConfigDict]
  ) -> List[int]:
      """Returns temporal dims merged into spatial dim for each view.
  
      In MTV, different views have different temporal dimensions. Before feeding
      tokens from different views into the multiview encoder, we reshape the tokens
      so that they have the same temporal dimension, part of the temporal dimensions
      are folded into the spatial dimesion.
  
      Args:
        num_frames: Number of frames in the input video.
        view_configs: Configurations of each view in the MTV model.
  
      Returns:
        Temporal dimensions that were merged into the spatial dimensions.
      """
      dims = get_input_token_temporal_dims(num_frames, view_configs)
      return [d // min(dims) for d in dims]
  
imports: from typing import Any, Dict, List, Optional, Sequence
  from absl import logging
  import flax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.projects.vivit import model_utils
  from scenic.train_lib import train_utils
  import scipy.ndimage
label_desc: Use image node_id 2 for calling a global function with example usage: get_temporal_dims_merged_into_space(num_frames, view_configs) and returns: unknown