code: def __init__(
      self,
      parent,
      batch_size=1,
      seq_length=2,
      alpha=1.0,
      beta=0.1,
      visual_prompter_type="framepad",
      visual_prompter_apply="replace",
      num_frames=2,
      max_img_size=448,
      visual_prompt_size=96,
      vocab_size=100,
      hidden_size=32,
      intermediate_size=32,
      num_hidden_layers=2,
      num_attention_heads=4,
      max_position_embeddings=30,
      max_grid_col_position_embeddings=30,
      max_grid_row_position_embeddings=30,
      hidden_dropout_prob=0.1,
      hidden_act="gelu",
      layer_norm_eps=1e-12,
      initializer_range=0.02,
      pad_token_id=0,
      type_vocab_size=2,
      attention_probs_dropout_prob=0.1,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.input_id_length = seq_length
      self.seq_length = (
          seq_length + 10 + 784
      )  # include text prompt length and visual input length
      self.alpha = alpha
      self.beta = beta
      self.visual_prompter_type = visual_prompter_type
      self.visual_prompter_apply = visual_prompter_apply
      self.num_frames = num_frames
      self.max_img_size = max_img_size
      self.visual_prompt_size = visual_prompt_size
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.intermediate_size = intermediate_size
      self.hidden_act = hidden_act
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.max_position_embeddings = max_position_embeddings
      self.max_grid_col_position_embeddings = (
          max_grid_col_position_embeddings
      )
      self.max_grid_row_position_embeddings = (
          max_grid_row_position_embeddings
      )
      self.layer_norm_eps = layer_norm_eps
      self.initializer_range = initializer_range
      self.pad_token_id = pad_token_id
      self.type_vocab_size = type_vocab_size
      self.is_training = False
      self.num_channels = 3
  
imports: import unittest
  from transformers import ResNetConfig, TvpConfig
  from transformers.testing_utils import require_torch, require_vision, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 to create a new TVPModelTester object  with example: obj = TVPModelTester(parent, batch_size, seq_length, alpha, beta, visual_prompter_type, visual_prompter_apply, num_frames, max_img_size, visual_prompt_size, vocab_size, hidden_size, intermediate_size, num_hidden_layers, num_attention_heads, max_position_embeddings, max_grid_col_position_embeddings, max_grid_row_position_embeddings, hidden_dropout_prob, hidden_act, layer_norm_eps, initializer_range, pad_token_id, type_vocab_size, attention_probs_dropout_prob)