class_docstr: A GLO encoder module, which is just a thin wrapper around nn.Embed.
  
  Attributes:
    num_embeddings: The number of embeddings.
    features: The dimensions of each embedding.
    embedding_init: The initializer to use for each.
docstr: Method to get embeddings for specified indices.
  
  Args:
    inputs: The indices to fetch embeddings for.
  
  Returns:
    The embeddings corresponding to the indices provided.
docstr_tok: ["Method","to","get","embeddings","for","specified","indices",".","Args",":","inputs",":","The","indices","to","fetch","embeddings","for",".","Returns",":","The","embeddings","corresponding","to","the","indices","provided","."]
code: def __call__(self, inputs: jnp.ndarray) -> jnp.ndarray:
      """Method to get embeddings for specified indices.
  
      Args:
        inputs: The indices to fetch embeddings for.
  
      Returns:
        The embeddings corresponding to the indices provided.
      """
      if inputs.shape[-1] == 1:
          inputs = jnp.squeeze(inputs, axis=-1)
  
      return self.embed(inputs)
  
imports: from flax import linen
  import jax.numpy
  from nerfies import types
label_desc: Use image node_id 2 for calling the GloEncoder obj's underlying member method code with example usage: obj.__call__(inputs) and returns: self