code: def create_model(
      model_name: str = None,
      context_length: int = 77,
      pretrained: bool = False,
      weights_path: str = None,
      device: Union[str, torch.device] = None,
  ) -> CLIP4Clip:
      """
      Create a CLIP4Clip model.
      Args:
          model_name (`str`):
              Model name, `clip_vit_b16` or `clip_vit_b32`.
          context_length (`int`):
              Length of context sequence, default is 77, the same as these in CLIP.
          pretrained (`bool`):
              Whether pretained model.
          weights_path (`str`):
              Your local trained weights path. default is None.
          device (`str`):
              Model device, `cpu` or `cuda`.
  
      Returns:
          (`CLIP4Clip`):
              CLIP4Clip model.
      >>> from towhee.models import clip4clip
      >>> model = clip4clip.create_model(model_name="clip_vit_b32", context_length=32, pretrained=False, device='cpu')
      >>> model.__class__.__name__
      'CLIP4Clip'
  
      """
      configs = get_configs(model_name)
      if "multilingual_model" in configs:
          configs.pop("multilingual_model")
      if "url" in configs:
          configs.pop("url")
      configs["context_length"] = context_length
      model = CLIP4Clip(**configs)
      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: import torch
  import logging
  from typing import Union
  from torch import nn
  from towhee.models.clip4clip.until_module import PreTrainedModel, CrossEn
  from towhee.models.clip import CLIP, get_configs
label_desc: Use image node_id 1 for calling a global function with example usage: create_model(model_name, context_length, pretrained, weights_path, device) and returns: model