code: def create_model(
      base_encoder: str = "clip_vit_b32",
      agg_module: str = "seqTransf",
      interaction: str = "wti",
      wti_arch: int = 2,
      cdcr: int = 3,
      cdcr_alpha1: float = 1.0,
      cdcr_alpha2: float = 0.06,
      cdcr_lambda: float = 0.001,
      cross_num_hidden_layers: int = None,
      pretrained: bool = False,
      weights_path: str = None,
      device: str = None,
  ) -> DRL:
      """
      Build a DRL model.
      Args:
          base_encoder (`str`):
              Base_encoder in DRL model, `clip_vit_b32` or `clip_vit_b16`.
          agg_module (`str`):
              Feature aggregation module for video. default: `seqTransf`, choices=[`ndone`, `seqLSTM`, `seqTransf`]
          interaction (`str`):
              Interaction type for retrieval. default: `wti`.
          wti_arch (`int`):
              Select an architecture for weight branch. default: 2.
          cdcr (`int`):
              Channel decorrelation regularization. default: 3.
          cdcr_alpha1 (`float`):
              Coefficient 1 for channel decorrelation regularization. default: 1.0.
          cdcr_alpha2 (`float`):
              Coefficient 2 for channel decorrelation regularization. default: 0.06.
          cdcr_lambda (`float`):
              Coefficient for channel decorrelation regularization. default: 0.001.
          cross_num_hidden_layers (`int`):
              Number of hidden layers for cross transformer interaction.
          pretrained (`bool`):
              Whether model is pretrained, default if False.
          weights_path (`str`):
              Pretrained model local path, default if None.
          device (`str`):
              Model device. `cpu` or `cuda`.
      Returns:
  
      >>> from towhee.models import drl
      >>> model = drl.create_model("clip_vit_b32")
      >>> model.__class__.__name__
      'DRL'
  
      """
      model = DRL(
          base_encoder=base_encoder,
          agg_module=agg_module,
          interaction=interaction,
          wti_arch=wti_arch,
          cdcr=cdcr,
          cdcr_alpha1=cdcr_alpha1,
          cdcr_alpha2=cdcr_alpha2,
          cdcr_lambda=cdcr_lambda,
          cross_num_hidden_layers=cross_num_hidden_layers,
      )
      if device is None:
          device = "cuda" if torch.cuda.is_available() else "cpu"
      model.to(device)
      if pretrained and weights_path is not None:
          state_dict = torch.load(weights_path, map_location=device)
          missing_keys = []
          unexpected_keys = []
          error_msgs = []
          metadata = getattr(state_dict, "_metadata", None)
          state_dict = state_dict.copy()
          if metadata is not None:
              state_dict._metadata = (
                  metadata  # pylint: disable=protected-access
              )
  
          def load(module, prefix=""):
              local_metadata = (
                  {}
                  if metadata is None
                  else metadata.get(prefix[:-1], {})
              )
              module._load_from_state_dict(  # pylint: disable=protected-access
                  state_dict,
                  prefix,
                  local_metadata,
                  True,
                  missing_keys,
                  unexpected_keys,
                  error_msgs,
              )
              for (
                  name,
                  child,
              ) in (
                  module._modules.items()
              ):  # pylint: disable=protected-access
                  if child is not None:
                      load(child, prefix + name + ".")
  
          load(model, prefix="")
  
          if len(missing_keys) > 0:
              logger.info(
                  "Weights of %s not initialized from pretrained model: %s",
                  model.__class__.__name__,
                  "\n   " + "\n   ".join(missing_keys),
              )
          if len(unexpected_keys) > 0:
              logger.info(
                  "Weights from pretrained model not used in %s: %s",
                  model.__class__.__name__,
                  "\n   " + "\n   ".join(unexpected_keys),
              )
          if len(error_msgs) > 0:
              logger.error(
                  "Weights from pretrained model cause errors in %s: %s",
                  model.__class__.__name__,
                  "\n   " + "\n   ".join(error_msgs),
              )
  
      if pretrained and weights_path is None:
          raise ValueError("weights_path is None")
      return model
  
imports: from collections import OrderedDict
  from typing import Optional
  from types import SimpleNamespace
  import torch
  import logging
  from torch import nn
  from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
  from towhee.models import clip
  from towhee.models.drl.until_module import convert_weights
  from towhee.models.drl.module_cross import CrossModel, Transformer
  from towhee.models.drl.until_module import LayerNorm, AllGather, AllGather2, CrossEn
label_desc: Use image node_id 1 for calling a global function with example usage: create_model(base_encoder, agg_module, interaction, wti_arch, cdcr, cdcr_alpha1, cdcr_alpha2, cdcr_lambda, cross_num_hidden_layers, pretrained, weights_path, device) and returns: model