code: def __init__(
      self,
      parent,
      batch_size=13,
      image_size=64,
      patch_size=2,
      num_channels=3,
      is_training=True,
      use_labels=True,
      hidden_size=32,
      num_hidden_layers=2,
      num_attention_heads=4,
      intermediate_size=37,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      type_sequence_label_size=10,
      initializer_range=0.02,
      backbone_featmap_shape=[1, 16, 4, 4],
      scope=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.image_size = image_size
      self.patch_size = patch_size
      self.num_channels = num_channels
      self.is_training = is_training
      self.use_labels = use_labels
      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.type_sequence_label_size = type_sequence_label_size
      self.initializer_range = initializer_range
      self.scope = scope
      self.backbone_featmap_shape = backbone_featmap_shape
  
      # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      # the number of patches is based on the feature map of the backbone, which by default uses an output stride
      # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
      num_patches = (self.image_size // 32) ** 2
      self.seq_length = num_patches + 1
  
imports: import unittest
  from transformers import ViTHybridConfig
  from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 to create a new ViTHybridModelTester object  with example: obj = ViTHybridModelTester(parent, batch_size, image_size, patch_size, num_channels, is_training, use_labels, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, type_sequence_label_size, initializer_range, backbone_featmap_shape, scope)