code: def __init__(
      self,
      parent,
      batch_size=13,
      seq_length=1024,  # speech is longer
      is_training=False,
      hidden_size=32,
      feat_extract_norm="group",
      feat_extract_dropout=0.0,
      feat_extract_activation="gelu",
      conv_dim=(64, 32, 32),
      conv_stride=(5, 2, 1),
      conv_kernel=(10, 3, 1),
      conv_bias=False,
      num_conv_pos_embeddings=31,
      num_conv_pos_embedding_groups=2,
      squeeze_factor=2,
      max_position_embeddings=512,
      position_buckets=256,
      share_att_key=True,
      relative_attention=True,
      position_biased_input=False,
      pos_att_type=("p2c", "c2p"),
      norm_rel_ebd="layer_norm",
      num_hidden_layers=2,
      num_attention_heads=2,
      hidden_dropout=0.1,
      intermediate_size=20,
      layer_norm_eps=1e-5,
      hidden_act="gelu",
      initializer_range=0.02,
      vocab_size=32,
      do_stable_layer_norm=False,
      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_norm = feat_extract_norm
      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.squeeze_factor = squeeze_factor
      self.max_position_embeddings = max_position_embeddings
      self.position_buckets = position_buckets
      self.share_att_key = share_att_key
      self.relative_attention = relative_attention
      self.position_biased_input = position_biased_input
      self.pos_att_type = pos_att_type
      self.norm_rel_ebd = norm_rel_ebd
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.hidden_dropout = hidden_dropout
      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.do_stable_layer_norm = do_stable_layer_norm
      self.scope = scope
  
      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.squeeze_factor
      )
  
imports: import math
  import unittest
  import pytest
  from transformers import SEWDConfig, is_torch_available
  from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  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 SEWDModelTester object  with example: obj = SEWDModelTester(parent, batch_size, seq_length, is_training, hidden_size, feat_extract_norm, feat_extract_dropout, feat_extract_activation, conv_dim, conv_stride, conv_kernel, conv_bias, num_conv_pos_embeddings, num_conv_pos_embedding_groups, squeeze_factor, max_position_embeddings, position_buckets, share_att_key, relative_attention, position_biased_input, pos_att_type, norm_rel_ebd, num_hidden_layers, num_attention_heads, hidden_dropout, intermediate_size, layer_norm_eps, hidden_act, initializer_range, vocab_size, do_stable_layer_norm, scope)