class_docstr: BERT.
class_docstr_tok: ["BERT","."]
code: def __call__(
      self,
      inputs: Dict[str, jnp.ndarray],
      *,
      train: bool,
      transfer_mode=False,
      debug: bool = False,
  ):
      x, word_embeddings = layers.Stem(
          vocab_size=self.stem_config.vocab_size,
          type_vocab_size=self.stem_config.type_vocab_size,
          hidden_size=self.stem_config.hidden_size,
          max_position_embeddings=self.stem_config.max_position_embeddings,
          dropout_rate=self.stem_config.dropout_rate,
          embedding_width=self.stem_config.get("embedding_width"),
          dtype=self.dtype,
          name="stem",
      )(
          input_word_ids=inputs["input_word_ids"],
          input_type_ids=inputs["input_type_ids"],
          input_mask=inputs["input_mask"],
          train=train,
      )
  
      x = layers.BERTEncoder(
          mlp_dim=self.encoder_config.mlp_dim,
          num_layers=self.encoder_config.num_layers,
          num_heads=self.encoder_config.num_heads,
          dropout_rate=self.encoder_config.dropout_rate,
          attention_dropout_rate=self.encoder_config.attention_dropout_rate,
          pre_norm=self.encoder_config.pre_norm,
          dtype=self.dtype,
          name="bert_encoder",
      )(x, input_mask=inputs["input_mask"], train=train)
  
      if self.head_config.type == "pretraining":
          next_sentence_prediction_logits = layers.ClassificationHead(
              num_outputs=2,
              hidden_sizes=(x.shape[-1], self.head_config.hidden_size),
              nonlinearity=nn.tanh,
              dtype=self.dtype,
              name="next_sentence_prediction_head",
          )(x, train=train)
          if transfer_mode:
              # Next sentence prediction head is a classification head and we can
              # reuse it for transfer evaluation on classification tasks.
              return next_sentence_prediction_logits
  
          masked_language_modeling_logits = (
              layers.MaskedLanguageModelHead(
                  dtype=self.dtype, name="masked_language_model_head"
              )(
                  x,
                  inputs["masked_lm_positions"],
                  word_embeddings,
                  train=train,
              )
          )
          return {
              "nsp_logits": next_sentence_prediction_logits,
              "mlm_logits": masked_language_modeling_logits,
          }
      elif self.head_config.type == "classification":
          return layers.ClassificationHead(
              num_outputs=self.head_config.num_classes,
              hidden_sizes=(x.shape[-1], self.head_config.hidden_size),
              nonlinearity=nn.tanh,
              dtype=self.dtype,
              name="classification_head",
          )(x, train=train)
      elif self.head_config.type == "regression":
          return layers.ClassificationHead(
              num_outputs=1,
              hidden_sizes=(x.shape[-1], self.head_config.hidden_size),
              nonlinearity=nn.tanh,
              dtype=self.dtype,
              name="regression_head",
          )(x, train=train)
  
imports: from typing import Any, Dict
  from absl import logging
  import flax
  import flax.linen
  import jax.numpy
  import ml_collections
  from scenic.common_lib import debug_utils
  from scenic.model_lib.base_models.classification_model import ClassificationModel
  from scenic.model_lib.base_models.regression_model import RegressionModel
  from scenic.projects.baselines.bert import bert_base_model
  from scenic.projects.baselines.bert import layers
label_desc: Use image node_id 1 for calling the BERT obj's underlying member method code with example usage: obj.__call__(inputs) and returns: dict, next_sentence_prediction_logits, layers, layers