code: def __init__(
      self,
      parent,
      batch_size=13,
      seq_length=1024,  # speech is longer
      is_training=False,
      hidden_size=16,
      feat_extract_dropout=0.0,
      feat_extract_activation="gelu",
      conv_dim=(32, 32, 32),
      conv_stride=(4, 4, 4),
      conv_kernel=(8, 8, 8),
      conv_bias=False,
      num_conv_pos_embeddings=16,
      num_conv_pos_embedding_groups=2,
      num_hidden_layers=4,
      num_attention_heads=2,
      hidden_dropout_prob=0.1,
      intermediate_size=20,
      layer_norm_eps=1e-5,
      hidden_act="gelu",
      initializer_range=0.02,
      mask_time_prob=0.5,
      mask_time_length=2,
      vocab_size=32,
      num_adapter_layers=1,
      adapter_stride=2,
      tdnn_dim=(32, 32),
      tdnn_kernel=(5, 3),
      tdnn_dilation=(1, 2),
      xvector_output_dim=32,
      scope=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.seq_length = seq_length
      self.is_training = is_training
      self.hidden_size = hidden_size
      self.feat_extract_dropout = feat_extract_dropout
      self.feat_extract_activation = feat_extract_activation
      self.conv_dim = conv_dim
      self.conv_stride = conv_stride
      self.conv_kernel = conv_kernel
      self.conv_bias = conv_bias
      self.num_conv_pos_embeddings = num_conv_pos_embeddings
      self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.hidden_dropout_prob = hidden_dropout_prob
      self.intermediate_size = intermediate_size
      self.layer_norm_eps = layer_norm_eps
      self.hidden_act = hidden_act
      self.initializer_range = initializer_range
      self.vocab_size = vocab_size
      self.num_adapter_layers = num_adapter_layers
      self.adapter_stride = adapter_stride
      self.mask_time_prob = mask_time_prob
      self.mask_time_length = mask_time_length
      self.scope = scope
      self.tdnn_dim = tdnn_dim
      self.tdnn_kernel = tdnn_kernel
      self.tdnn_dilation = tdnn_dilation
      self.xvector_output_dim = xvector_output_dim
  
      output_seq_length = self.seq_length
      for kernel, stride in zip(self.conv_kernel, self.conv_stride):
          output_seq_length = (
              output_seq_length - (kernel - 1)
          ) / stride
      self.output_seq_length = int(math.ceil(output_seq_length))
      self.encoder_seq_length = self.output_seq_length
  
      self.adapter_output_seq_length = (
          self.output_seq_length - 1
      ) // adapter_stride + 1
  
imports: import math
  import unittest
  import numpy
  from datasets import load_dataset
  from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
  from transformers import Data2VecAudioConfig, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_soundfile, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init
label_desc: Use image node_id 1 to create a new Data2VecAudioModelTester object  with example: obj = Data2VecAudioModelTester(parent, batch_size, seq_length, is_training, hidden_size, feat_extract_dropout, feat_extract_activation, conv_dim, conv_stride, conv_kernel, conv_bias, num_conv_pos_embeddings, num_conv_pos_embedding_groups, num_hidden_layers, num_attention_heads, hidden_dropout_prob, intermediate_size, layer_norm_eps, hidden_act, initializer_range, mask_time_prob, mask_time_length, vocab_size, num_adapter_layers, adapter_stride, tdnn_dim, tdnn_kernel, tdnn_dilation, xvector_output_dim, scope)