class_docstr: Transformers handler class for  MMFTransformerWithVideoAudio model.
class_docstr_tok: ["Transformers","handler","class","for","MMFTransformerWithVideoAudio","model","."]
code: def initialize(self, ctx):
      self.manifest = ctx.manifest
      properties = ctx.system_properties
      model_dir = properties.get("model_dir")
      serialized_file = self.manifest["model"]["serializedFile"]
      model_pt_path = os.path.join(model_dir, serialized_file)
      self.map_location = "cuda" if torch.cuda.is_available() else "cpu"
      self.device = torch.device(
          self.map_location + ":" + str(properties.get("gpu_id"))
          if torch.cuda.is_available()
          else self.map_location
      )
  
      # reading the csv file which include all the labels in the dataset to make the class/index mapping
      # and matching the output of the model with num labels from dataset
      df = pd.read_csv("./charades_action_lables.csv")
      label_set = set()
      df["action_labels"] = df["action_labels"].str.replace('"', "")
      labels_initial = df["action_labels"].tolist()
      labels = []
      for sublist in labels_initial:
          new_sublist = ast.literal_eval(sublist)
          labels.append(new_sublist)
          for item in new_sublist:
              label_set.add(item)
      classes = sorted(list(label_set))
      self.class_to_idx = {classes[i]: i for i in range(len(classes))}
      self.classes = classes
      self.labels = labels
      self.idx_to_class = classes
      config = OmegaConf.load("config.yaml")
      print("*********** config keyssss **********", config.keys())
      setup_very_basic_config()
      setup_imports()
      self.model = MMFTransformer(config.model_config.mmf_transformer)
      self.model.build()
      self.model.init_losses()
      self.processor = build_processors(
          config.dataset_config["charades"].processors
      )
      state_dict = torch.load(serialized_file, map_location=self.device)
      self.model.load_state_dict(state_dict)
      self.model.to(self.device)
      self.model.eval()
      self.initialized = True
      print(
          "********* files in temp direcotry that .mar file got extracted *********",
          os.listdir(model_dir),
      )
  
imports: import json
  import logging
  import os
  import pickle
  import sys
  import ast
  import torch
  from ts.torch_handler.base_handler import BaseHandler
  import io
  import torchaudio
  import torchvision
  from omegaconf import OmegaConf
  import pandas
  import csv
  from torchvision import transforms
  from torchvision.datasets.vision import VisionDataset
  from torchvision.io import read_video_timestamps, read_video
  from mmf.common.sample import Sample, SampleList
  from mmf.utils.env import set_seed, setup_imports
  from mmf.utils.logger import setup_logger, setup_very_basic_config
  from mmf.datasets.base_dataset import BaseDataset
  from mmf.utils.build import build_encoder, build_model, build_processors
  from mmf.datasets.mmf_dataset_builder import MMFDatasetBuilder
  from torch.utils.data import IterableDataset
  from mmf.utils.configuration import load_yaml
  from mmf.models.mmf_transformer import MMFTransformer
label_desc: Use image node_id 2 for calling the MMFHandler obj's underlying member method code with example usage: obj.initialize(ctx) without return types