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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 decode_from_file(estimator, vocabulary, model_type, batch_size, sequence_length, checkpoint_path="", input_filename=gin.REQUIRED, output_filename=gin.REQUIRED, eos_id=1):
 """Decode from a text file. Args: estimator: a TPUEstimator vocabulary: a mtf.transformer.vocabulary.Vocabulary model_type: a string batch_size: an integer sequence_length: an integer (maximum decode length) checkpoint_path: an optional string input_filename: a string output_filename: a string eos_id: EOS id """

  with tf.gfile.Open(input_filename) as f:
    text = f.read()
  records = text.split("\n")
  inputs = [record.strip() for record in records]
  # Strip the last empty line.
  if not inputs[-1]:
    inputs.pop()
  n = len(inputs)
  # encode all inputs
  all_input_ids = []
  for line in inputs:
    ids = inputs_vocabulary(vocabulary).encode(line.strip())
    if model_type != "lm":
      # for text2self problems, the inputs represent a partial sequence
      # to be continued, and should not be terminated by EOS.
      # for sequence-to-sequence problems, the input needs to be EOS-terminated
      ids += [eos_id]
    if len(ids) > sequence_length:
      ids = ids[:sequence_length]
    else:
      ids.extend([0] * (sequence_length - len(ids)))
      all_input_ids.append(ids)
      # pad to make an integral number of batches
  all_input_ids.extend([all_input_ids[0]] * (-n % batch_size))
  padded_n = len(all_input_ids)
  all_input_ids = np.array(all_input_ids, dtype=np.int32)

  def input_fn(params):
    del params
    dataset = tf.data.Dataset.from_tensor_slices({"inputs": all_input_ids})
    dataset = dataset.batch(batch_size, drop_remainder=True)
    return dataset

  result_iter = estimator.predict(input_fn, checkpoint_path=checkpoint_path)
  vocab_size = targets_vocabulary(vocabulary).vocab_size
  decodes = []
  for i, result in enumerate(result_iter):
    output_ids = clean_decodes(list(result["outputs"]), vocab_size)
    output_string = targets_vocabulary(vocabulary).decode(
        [int(x) for x in output_ids])
    decodes.append(output_string)
    if i & (i - 1) == 0:
      if i < len(inputs):
        # LOG every power of 2, don't log if it's padded input i >= len(inputs)
        tf.logging.info("decode %d input = %s" % (i, inputs[i]))
        tf.logging.info("          output = %s" % output_string)

  # BUG WORKAROUND - on TF1.13 and earlier, the output for each batch is
  # repeated a number of times equal to the number of cores.
  if len(decodes) == padded_n:
    tf.logging.info("number of decodes matches number of inputs")
  elif len(decodes) % padded_n == 0:
    num_cores = len(decodes) // padded_n
    tf.logging.info("output is repeated num_cores times - removing extras")

    def keep(i):
      return i % (batch_size * num_cores) < batch_size

    decodes = [d for i, d in enumerate(decodes) if keep(i)]
  else:
    raise ValueError("unexpected number of outputs")
  output_file = tf.gfile.Open(output_filename, "w")
  decodes = decodes[:n]
  for d in decodes:
    output_file.write(d)
    output_file.write("\n")
  output_file.close()