class_docstr: Adds learned positional embeddings to the inputs.
  
  Attributes:
    posemb_init: Positional embedding initializer.
  
  Returns:
    Output in shape `[bs, timesteps, in_dim]`.
code: def __call__(self, inputs: jnp.ndarray) -> jnp.ndarray:
      # Inputs.shape is (batch_size, seq_len, emb_dim).
      assert inputs.ndim == 3, (
          "Number of dimensions should be 3,"
          " but it is: %d" % inputs.ndim
      )
      pos_emb_shape = (1, inputs.shape[1], inputs.shape[2])
      pe = self.param(
          "pos_embedding", self.posemb_init, pos_emb_shape, inputs.dtype
      )
      return inputs + pe
  
imports: from typing import Any, Callable, Optional, Sequence
  from absl import logging
  import flax
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.base_models.multilabel_classification_model import MultiLabelClassificationModel
  from scenic.model_lib.layers import attention_layers
  from scenic.model_lib.layers import nn_layers
  import scipy
label_desc: Use image node_id 1 for calling the AddPositionEmbs obj's underlying member method code with example usage: obj.__call__(inputs) and returns: unknown