code: def __init__(
      self,
      parent,
      batch_size=13,
      seq_length=1024,
      is_training=False,
      hidden_size=16,
      feat_extract_norm="group",
      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,  # this is most likely not correctly set yet
      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.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.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
  
imports: import copy
  import inspect
  import math
  import unittest
  import numpy
  import pytest
  from transformers import is_tf_available
  from transformers.testing_utils import require_soundfile, require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
label_desc: Use image node_id 1 to create a new TFHubertModelTester object  with example: obj = TFHubertModelTester(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, num_hidden_layers, num_attention_heads, hidden_dropout_prob, intermediate_size, layer_norm_eps, hidden_act, initializer_range, vocab_size, do_stable_layer_norm, scope)