code: def __init__(
      self,
      parent,
      batch_size=8,
      is_training=True,
      use_labels=True,
      hidden_size=256,
      num_hidden_layers=2,
      num_attention_heads=8,
      intermediate_size=4,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      num_queries=12,
      num_channels=3,
      min_size=200,
      max_size=200,
      n_targets=8,
      num_labels=91,
  ):
      self.parent = parent
      self.batch_size = batch_size
      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.num_queries = num_queries
      self.num_channels = num_channels
      self.min_size = min_size
      self.max_size = max_size
      self.n_targets = n_targets
      self.num_labels = num_labels
  
      # we also set the expected seq length for both encoder and decoder
      self.encoder_seq_length = math.ceil(
          self.min_size / 32
      ) * math.ceil(self.max_size / 32)
      self.decoder_seq_length = self.num_queries
  
imports: import inspect
  import math
  import unittest
  from transformers import ConditionalDetrConfig, is_timm_available, is_vision_available
  from transformers.testing_utils import require_timm, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
label_desc: Use image node_id 1 to create a new ConditionalDetrModelTester object  with example: obj = ConditionalDetrModelTester(parent, batch_size, is_training, use_labels, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, num_queries, num_channels, min_size, max_size, n_targets, num_labels)