code: def __init__(
      self,
      parent,
      batch_size=10,
      seq_length=40,  # speech is longer
      is_training=False,
      vocab_size=32,
      hidden_size=128 * 4,
      num_hidden_layers=4,
      intermediate_size=20,
      num_attention_heads=4,
      attention_head_dim=128,
      max_position_embeddings=920,
      layer_norm_eps=1e-5,
      layerdrop=0.3,
      hidden_act="relu",
      initializer_range=0.02,
      hidden_dropout_prob=0.3,
      attention_probs_dropout_prob=0.3,
      conv_glu_dim=1,
      conv_dropout=0.3,
      num_conv_layers=1,
      conv_kernel=(7,),
      conv_stride=(3,),
      input_feat_per_channel=80,
      input_channels=1,
      conv_channels=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.seq_length = seq_length  # speech is longer
      self.is_training = is_training
  
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.intermediate_size = intermediate_size
      self.num_attention_heads = num_attention_heads
  
      self.attention_head_dim = attention_head_dim
      self.max_position_embeddings = max_position_embeddings
  
      self.layer_norm_eps = layer_norm_eps
      self.layerdrop = layerdrop
      self.hidden_act = hidden_act
      self.initializer_range = initializer_range
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
  
      self.conv_glu_dim = conv_glu_dim
      self.conv_dropout = conv_dropout
      self.num_conv_layers = num_conv_layers
      self.conv_kernel = conv_kernel
      self.conv_stride = conv_stride
      self.input_feat_per_channel = input_feat_per_channel
      self.input_channels = input_channels
      self.conv_channels = conv_channels
  
      output_seq_length = self.seq_length
      dilation = 1
      for _, kernel_sz, stride in zip(
          range(self.num_conv_layers),
          self.conv_kernel,
          self.conv_stride,
      ):
          padding = kernel_sz // 2
          output_seq_length = (
              output_seq_length
              + 2 * padding
              - dilation * (kernel_sz - 1)
              - 1
          )
          output_seq_length = (
              torch.div(
                  output_seq_length, stride, rounding_mode="trunc"
              )
              + 1
          )
  
      self.output_seq_length = int(math.ceil(output_seq_length))
      self.encoder_seq_length = self.output_seq_length
  
imports: import inspect
  import math
  import unittest
  from datasets import load_dataset
  from transformers import MCTCTConfig, 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
label_desc: Use image node_id 1 to create a new MCTCTModelTester object  with example: obj = MCTCTModelTester(parent, batch_size, seq_length, is_training, vocab_size, hidden_size, num_hidden_layers, intermediate_size, num_attention_heads, attention_head_dim, max_position_embeddings, layer_norm_eps, layerdrop, hidden_act, initializer_range, hidden_dropout_prob, attention_probs_dropout_prob, conv_glu_dim, conv_dropout, num_conv_layers, conv_kernel, conv_stride, input_feat_per_channel, input_channels, conv_channels)