code: def imagebind_huge(pretrained=False):
      model = ImageBindModel(
          vision_embed_dim=1280,
          vision_num_blocks=32,
          vision_num_heads=16,
          text_embed_dim=1024,
          text_num_blocks=24,
          text_num_heads=16,
          out_embed_dim=1024,
          audio_drop_path=0.1,
          imu_drop_path=0.7,
      )
  
      if pretrained:
          if not os.path.exists(".checkpoints/imagebind_huge.pth"):
              print(
                  "Downloading imagebind weights to .checkpoints/imagebind_huge.pth ..."
              )
              os.makedirs(".checkpoints", exist_ok=True)
              torch.hub.download_url_to_file(
                  "https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth",
                  ".checkpoints/imagebind_huge.pth",
                  progress=True,
              )
  
          model.load_state_dict(
              torch.load(".checkpoints/imagebind_huge.pth")
          )
  
      return model
  
file_path: imagebind/imagebind/models/imagebind_model.py
imports: import os
  from functools import partial
  from types import SimpleNamespace
  import torch
  import torch.nn
  from imagebind.models.helpers import EinOpsRearrange, LearnableLogitScaling, Normalize, SelectElement, SelectEOSAndProject
  from imagebind.models.multimodal_preprocessors import AudioPreprocessor, IMUPreprocessor, PadIm2Video, PatchEmbedGeneric, RGBDTPreprocessor, SpatioTemporalPosEmbeddingHelper, TextPreprocessor, ThermalPreprocessor
  from imagebind.models.transformer import MultiheadAttention, SimpleTransformer
label_desc: Use image node_id 1 for calling a global function with example usage: imagebind_huge(pretrained) and returns: model