class_docstr: Dense video captioning module that encodes a video and generate tokens.
code: def encode(self, encoder_inputs, *, train=True):
      # load modalities
      if "features" in encoder_inputs:
          features = encoder_inputs["features"]
      else:
          features = None
  
      if "text" in encoder_inputs:
          encoder_input_tokens = encoder_inputs["text"]
      else:
          encoder_input_tokens = None
  
      if self.config.encoder.encoder_type in [
          "t5_encoder",
          "cat_encoder",
      ]:  # give correct arguments
          return self.encoder(
              features=features,
              encoder_input_tokens=encoder_input_tokens,
              encoder_segment_ids=None,
              enable_dropout=train,
          )  # pytype: disable=wrong-keyword-args
      return self.encoder(features, train=train)
  
imports: import functools
  from typing import Any, Dict, Mapping, Optional, Tuple
  from absl import logging
  import flax.linen
  from flax.training import common_utils
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib.base_models import base_model
  from scenic.model_lib.base_models import model_utils
  from scenic.projects.baselines import vit
  from scenic.projects.t5 import layers
  from scenic.projects.t5 import model
  from t5x import decoding
  from t5x.models import DecodeFnCallable
label_desc: Use image node_id 4 for calling the DenseVideoCaptioningModule obj's underlying member method code with example usage: obj.encode(encoder_inputs) and returns: self, self