def __init__(self, input_dim, minval, maxval, **kwargs):
    """Constructs layer.

    Args:
      input_dim: int > 0. Size of the vocabulary, i.e. maximum integer index +
        1.
      minval: Lowest inclusive value of n for computing n-grams. For example,
        setting it to 1 will compute starting from unigrams.
      maxval: Highest non-inclusive value of n for computing n-grams. For
        example, setting it to 3 will compute at most bigrams.
      **kwargs: kwargs of parent class.
    """
    super(NGram, self).__init__(**kwargs)
    self.input_dim = input_dim
    self.minval = minval
    self.maxval = maxval
Constructs layer.

    Args:
      input_dim: int > 0. Size of the vocabulary, i.e. maximum integer index +
        1.
      minval: Lowest inclusive value of n for computing n-grams. For example,
        setting it to 1 will compute starting from unigrams.
      maxval: Highest non-inclusive value of n for computing n-grams. For
        example, setting it to 3 will compute at most bigrams.
      **kwargs: kwargs of parent class.
    
tensor2tensor/layers/ngram.py