class_docstr: Creates learned positional embeddings for object queries.
  
  Attributes:
    hidden_dim: Hidden dimension for the pos embeddings.
    num_queries: Number of object queries.
    posemb_init: Positional embeddings initializer.
    dtype: Jax dtype; The dtype of the computation (default: float32).
class_docstr_tok: ["Creates","learned","positional","embeddings","for","object","queries",".","Attributes",":","hidden_dim",":","Hidden","dimension","for","the","pos","embeddings",".","num_queries",":","Number","of","object","queries",".","posemb_init",":","Positional","embeddings","initializer",".","dtype",":","Jax","dtype",";","The","dtype","of","the","computation","(","default",":","float32",")","."]
docstr: Creates the positional embeddings for queries.
  
  Returns:
    Positional embedding for object queries.
code: def __call__(self) -> jnp.ndarray:
      """Creates the positional embeddings for queries.
  
      Returns:
        Positional embedding for object queries.
      """
      query_pos = self.param(
          "query_emb",
          self.posemb_init,
          (self.num_queries, self.hidden_dim),
      )
      query_pos = jnp.expand_dims(query_pos, 0)
      return jnp.asarray(query_pos, self.dtype)
  
imports: import functools
  from typing import Any, Callable, Dict, Tuple, Optional
  import flax.linen
  import jax
  from jax.nn import initializers
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.layers import attention_layers
  from scenic.projects.baselines import resnet
  from scenic.projects.baselines.detr import detr_base_model
label_desc: Use image node_id 1 for calling the QueryPosEmbedding obj's underlying member method code with example usage: obj.__call__() and returns: jnp