code: def generate_inputs_for_model(
      model_cls, model, model_name, bs, device, include_loss_args=False
  ):
      # TODO - Check if following values are representative
      num_choices = 3
      num_visual_features = 42
      seq_length = get_sequence_length(model_cls, model_name)
      vocab_size = model.config.vocab_size
  
      if model_name.startswith("Wav2Vec2"):
          # TODO: If we add more input_values style models, try to work this
          # into the overall control flow
          target_length = 100
          return {
              "input_values": torch.randn(
                  (bs, seq_length), device=device
              ),
              # Added because that's what the example training script has
              "attention_mask": rand_int_tensor(
                  device, 0, 2, (bs, seq_length)
              ),
              "labels": rand_int_tensor(
                  device, 0, vocab_size, (bs, target_length)
              ),
          }
  
      if model_name.endswith("MultipleChoice"):
          input = rand_int_tensor(
              device, 0, vocab_size, (bs, num_choices, seq_length)
          )
      elif model_name.startswith("Roberta"):
          input = rand_int_tensor(device, 0, 1, (bs, seq_length))
      else:
          input = rand_int_tensor(
              device, 0, vocab_size, (bs, seq_length)
          )
  
      if "Bart" in model_name:
          input[:, -1] = model.config.eos_token_id
  
      input_dict = {"input_ids": input}
  
      if (
          model_name.startswith("T5")
          or model_name.startswith("M2M100")
          or model_name.startswith("MT5")
          or model_cls
          in [
              BlenderbotModel,
              BlenderbotSmallModel,
              BlenderbotForConditionalGeneration,
              BlenderbotSmallForConditionalGeneration,
              PegasusModel,
              PegasusForConditionalGeneration,
              MarianModel,
              MarianMTModel,
          ]
      ):
          input_dict["decoder_input_ids"] = input
  
      if model_name.startswith("Lxmert"):
          visual_feat_dim, visual_pos_dim = (
              model.config.visual_feat_dim,
              model.config.visual_pos_dim,
          )
          input_dict["visual_feats"] = torch.randn(
              bs, num_visual_features, visual_feat_dim
          )
          input_dict["visual_pos"] = torch.randn(
              bs, num_visual_features, visual_pos_dim
          )
  
      if include_loss_args:
          if model_name.endswith("PreTraining"):
              if model_cls in [
                  ElectraForPreTraining,
                  LxmertForPreTraining,
              ]:
                  input_dict["labels"] = rand_int_tensor(
                      device, 0, 1, (bs, seq_length)
                  )
              else:
                  label_name = (
                      "sentence_order_label"
                      if model_cls in [AlbertForPreTraining]
                      else "next_sentence_label"
                  )
                  input_dict["labels"] = (
                      rand_int_tensor(
                          device, 0, vocab_size, (bs, seq_length)
                      ),
                  )
                  input_dict[label_name] = rand_int_tensor(
                      device, 0, 1, (bs,)
                  )
          elif model_name.endswith("QuestionAnswering"):
              input_dict["start_positions"] = rand_int_tensor(
                  device, 0, seq_length, (bs,)
              )
              input_dict["end_positions"] = rand_int_tensor(
                  device, 0, seq_length, (bs,)
              )
          elif (
              model_name.endswith("MaskedLM")
              or model_name.endswith("HeadModel")
              or model_name.endswith("CausalLM")
              or model_name.endswith("DoubleHeadsModel")
          ):
              input_dict["labels"] = rand_int_tensor(
                  device, 0, vocab_size, (bs, seq_length)
              )
          elif model_name.endswith("TokenClassification"):
              input_dict["labels"] = rand_int_tensor(
                  device,
                  0,
                  model.config.num_labels - 1,
                  (bs, seq_length),
              )
          elif model_name.endswith("MultipleChoice"):
              input_dict["labels"] = rand_int_tensor(
                  device, 0, num_choices, (bs,)
              )
          elif model_name.endswith("SequenceClassification"):
              input_dict["labels"] = rand_int_tensor(
                  device, 0, model.config.num_labels - 1, (bs,)
              )
          elif model_name.endswith("NextSentencePrediction"):
              input_dict["labels"] = rand_int_tensor(
                  device, 0, 1, (bs,)
              )
          elif model_name.endswith("ForConditionalGeneration"):
              input_dict["labels"] = rand_int_tensor(
                  device, 0, vocab_size - 1, (bs, seq_length)
              )
          elif model_name in EXTRA_MODELS:
              input_dict["labels"] = rand_int_tensor(
                  device, 0, vocab_size, (bs, seq_length)
              )
          else:
              raise NotImplementedError(
                  f"Class {model_name} unsupported for training test "
              )
  
      return input_dict
  
imports: import importlib
  import logging
  import os
  import re
  import subprocess
  import sys
  import warnings
  import torch
  from common import BenchmarkRunner, download_retry_decorator, main, reset_rng_state
  from torch._dynamo.testing import collect_results
  from torch._dynamo.utils import clone_inputs
label_desc: Use image node_id 4 for calling a global function with example usage: generate_inputs_for_model(model_cls, model, model_name, bs, device, include_loss_args) and returns: input_dict, dict