class_docstr: Specify a new class for doing the embedding stuff as Flax's one use 'embedding' for the parameter name and PyTorch
  use 'weight'
class_docstr_tok: ["Specify","a","new","class","for","doing","the","embedding","stuff","as","Flax","'s","one","use","'embedding","'","for","the","parameter","name","and","PyTorch","use","'weight","'"]
code: def __call__(self, inputs):
      embedding = self.param(
          "weight", self.emb_init, (self.vocab_size, self.hidden_size)
      )
      return jnp.take(embedding, inputs, axis=0)
  
imports: from typing import Callable, Dict, Tuple
  import numpy
  import flax.linen
  import jax
  import jax.numpy
  from jax.random import PRNGKey
  from modeling_flax_performer_utils import make_fast_softmax_attention
  from transformers.file_utils import add_start_docstrings
  from transformers.modeling_flax_utils import ACT2FN
  from transformers.models.bert.configuration_bert import BertConfig
  from transformers.models.bert.modeling_flax_bert import FlaxBertOnlyMLMHead, FlaxBertPreTrainedModel
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling the FlaxPerformerEmbedding obj's underlying member method code with example usage: obj.__call__(inputs) and returns: jnp