class_docstr: Add a cls token and apply a spatial-temporal encoding to a tensor.
  Args:
      embed_dim(int):
          Embedding dimension for input sequence.
      patch_embed_shape(Tuple):
          The number of patches in each dimension (T, H, W) after patch embedding.
      sep_pos_embed(bool):
          If set to true, one positional encoding is used for
          spatial patches and another positional encoding is used for temporal
          sequence. Otherwise, only one positional encoding is used for all the patches.
      has_cls(bool):
          If set to true, a cls token is added in the beginning of each input sequence.
code: def get_patch_embed_shape(self):
      return self.patch_embed_shape
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the SpatialTemporalClsPositionalEncoding obj's underlying member method code with example usage: obj.get_patch_embed_shape() and returns: self