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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def transformer_librispeech_v2():
 """HParams for training ASR model on LibriSpeech V2."""

  hparams = transformer_base()

  hparams.max_length = 1240000
  hparams.max_input_seq_length = 1550
  hparams.max_target_seq_length = 350
  hparams.batch_size = 16
  hparams.num_decoder_layers = 4
  hparams.num_encoder_layers = 6
  hparams.hidden_size = 384
  hparams.learning_rate = 0.15
  hparams.daisy_chain_variables = False
  hparams.filter_size = 1536
  hparams.num_heads = 2
  hparams.ffn_layer = "conv_relu_conv"
  hparams.conv_first_kernel = 9
  hparams.weight_decay = 0
  hparams.layer_prepostprocess_dropout = 0.2
  hparams.relu_dropout = 0.2

  return hparams