def ConfigurableTransformerLM(vocab_size,
                              d_model=512,
                              d_ff=2048,
                              n_layers=6,
                              n_heads=8,
                              max_len=2048,
                              dropout=0.1,
                              dropout_shared_axes=None,
                              mode='train',
                              ff_activation=tl.Relu,
                              ff_dropout=0.1,
                              ff_chunk_size=0,
                              ff_use_sru=0,
                              ff_sparsity=0,
                              ff_sparsity_type='1inN',
                              loss_sparsity_type='mult',
                              loss_sparsity=0,
                              loss_d_lowrank=0,
                              loss_sparsity_prob=None,
                              attention_chunk_size=0,
                              attention_type=tl.CausalAttention,
                              pos_type=None,
                              pos_axial_shape=None,
                              pos_d_axial_embs=None,
                              pos_start_from_zero_prob=1.0,
                              pos_max_offset_to_add=0):
  """Returns a Transformer language model.

  This model performs autoregressive language modeling:

    - input: rank 2 tensor representing a batch of text strings via token IDs
      plus padding markers; shape is (batch_size, sequence_length). The tensor
      elements are integers in `range(vocab_size)`, and `0` values mark padding
      positions.

    - output: rank 3 tensor representing a batch of log-probability
      distributions for each sequence position over possible token IDs;
      shape is (batch_size, sequence_length, `vocab_size`).

  This model uses only the decoder part of the overall Transformer.

  Args:
    vocab_size: Input vocabulary size -- each element of the input tensor should
      be an integer in `range(vocab_size)`. These integers typically represent
      token IDs from a vocabulary-based tokenizer.
    d_model: Final dimension of tensors at most points in the model, including
      the initial embedding output.
    d_ff: Size of special dense layer in the feed-forward part of each encoder
      block.
    n_layers: Number of encoder blocks. Each block includes attention, dropout,
      residual, feed-forward (`Dense`), and activation layers.
    n_heads: Number of attention heads.
    max_len: Maximum symbol length for positional encoding.
    dropout: Stochastic rate (probability) for dropping an activation value when
      applying dropout within an encoder block.
    dropout_shared_axes: Tensor axes on which to share a dropout mask. Sharing
      along batch and sequence axes (`dropout_shared_axes=(0,1)`) is a useful
      way to save memory and apply consistent masks to activation vectors at
      different sequence positions.
    mode: If `'predict'`, use fast inference. If `'train'`, each encoder block
      will include dropout; else, it will pass all values through unaltered.
    ff_activation: Type of activation function at the end of each encoder block;
      must be an activation-type subclass of `Layer`.
    ff_dropout: Stochastic rate (probability) for dropping an activation value
      when applying dropout after the FF dense layer.
    ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
    ff_use_sru: int or pair of ints; if > 0, we use this many SRU layers
      in addition to the feed-forward block (second int specifies sru size)
    ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
    ff_sparsity_type: string, if ff_sparsity >0,
      use SparseFF if ff_sparsity_type=`'1inN'` and
      use BlockSparseFF if ff_sparsity_type=`'Block'`
    loss_sparsity_type: string, type of sparsity to used in loss layer. See
      SparseDenseWithOptions for options. None if no sparsity should be used.
    loss_sparsity: int, the sparsity for loss layer (if used)
    loss_d_lowrank: int, the dimensions for intermediate layer (if used)
    loss_sparsity_prob: float, the probability for sparse version of loss to be
      used. If None, only sparse version is used.
    attention_chunk_size: int, if > 0 run attention chunked at this size
    attention_type: The attention layer to use for the decoder part.
    pos_type: string, the type of positional embeddings to use.
    pos_axial_shape: tuple of ints: input shape to use for the axial position
      encoding. If unset, axial position encoding is disabled.
    pos_d_axial_embs: tuple of ints: depth of position embedding for each axis.
      Tuple length must match pos_axial_shape, and values must sum to d_model.
    pos_start_from_zero_prob: how often to start from 0 during training,
      (if 1.0, we always start from position 0, if less, we randomize).
    pos_max_offset_to_add: maximum offset to add to positions during training
      when randomizing; this offset plus input length must still be less than
      max_len for all training examples.

  Returns:
    A Transformer language model as a layer that maps from a tensor of tokens
    to activations over a vocab set.
  """
  positional_encoder = [
      tl.Embedding(vocab_size, d_model),
      tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
      PositionalEncoder(
          mode, dropout, max_len, pos_type, pos_axial_shape, pos_d_axial_embs,
          pos_start_from_zero_prob, pos_max_offset_to_add)
  ]

  # pylint: disable=g-complex-comprehension
  decoder_blocks = [
      DecoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes, mode,
                   ff_activation, ff_dropout, ff_chunk_size, ff_use_sru,
                   ff_sparsity, ff_sparsity_type,
                   attention_chunk_size, attention_type)
      for i in range(n_layers)
  ]
  # pylint: enable=g-complex-comprehension

  # Assemble and return the model.
  return tl.Serial(               # tokens (or chunked tuple of tokens)
      tl.ShiftRight(mode=mode),   # toks
      positional_encoder,         # vecs
      decoder_blocks,             # vecs
      tl.LayerNorm(),             # vecs
      tl.SparseDenseWithOptions(  # vecs
          vocab_size, d_input=d_model, sparsity_type=loss_sparsity_type,
          sparsity=loss_sparsity, d_lowrank=loss_d_lowrank,
          prob_sparse=loss_sparsity_prob, mode=mode),
  )
Returns a Transformer language model.

  This model performs autoregressive language modeling:

    - input: rank 2 tensor representing a batch of text strings via token IDs
      plus padding markers; shape is (batch_size, sequence_length). The tensor
      elements are integers in `range(vocab_size)`, and `0` values mark padding
      positions.

    - output: rank 3 tensor representing a batch of log-probability
      distributions for each sequence position over possible token IDs;
      shape is (batch_size, sequence_length, `vocab_size`).

  This model uses only the decoder part of the overall Transformer.

  Args:
    vocab_size: Input vocabulary size -- each element of the input tensor should
      be an integer in `range(vocab_size)`. These integers typically represent
      token IDs from a vocabulary-based tokenizer.
    d_model: Final dimension of tensors at most points in the model, including
      the initial embedding output.
    d_ff: Size of special dense layer in the feed-forward part of each encoder
      block.
    n_layers: Number of encoder blocks. Each block includes attention, dropout,
      residual, feed-forward (`Dense`), and activation layers.
    n_heads: Number of attention heads.
    max_len: Maximum symbol length for positional encoding.
    dropout: Stochastic rate (probability) for dropping an activation value when
      applying dropout within an encoder block.
    dropout_shared_axes: Tensor axes on which to share a dropout mask. Sharing
      along batch and sequence axes (`dropout_shared_axes=(0,1)`) is a useful
      way to save memory and apply consistent masks to activation vectors at
      different sequence positions.
    mode: If `'predict'`, use fast inference. If `'train'`, each encoder block
      will include dropout; else, it will pass all values through unaltered.
    ff_activation: Type of activation function at the end of each encoder block;
      must be an activation-type subclass of `Layer`.
    ff_dropout: Stochastic rate (probability) for dropping an activation value
      when applying dropout after the FF dense layer.
    ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
    ff_use_sru: int or pair of ints; if > 0, we use this many SRU layers
      in addition to the feed-forward block (second int specifies sru size)
    ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
    ff_sparsity_type: string, if ff_sparsity >0,
      use SparseFF if ff_sparsity_type=`'1inN'` and
      use BlockSparseFF if ff_sparsity_type=`'Block'`
    loss_sparsity_type: string, type of sparsity to used in loss layer. See
      SparseDenseWithOptions for options. None if no sparsity should be used.
    loss_sparsity: int, the sparsity for loss layer (if used)
    loss_d_lowrank: int, the dimensions for intermediate layer (if used)
    loss_sparsity_prob: float, the probability for sparse version of loss to be
      used. If None, only sparse version is used.
    attention_chunk_size: int, if > 0 run attention chunked at this size
    attention_type: The attention layer to use for the decoder part.
    pos_type: string, the type of positional embeddings to use.
    pos_axial_shape: tuple of ints: input shape to use for the axial position
      encoding. If unset, axial position encoding is disabled.
    pos_d_axial_embs: tuple of ints: depth of position embedding for each axis.
      Tuple length must match pos_axial_shape, and values must sum to d_model.
    pos_start_from_zero_prob: how often to start from 0 during training,
      (if 1.0, we always start from position 0, if less, we randomize).
    pos_max_offset_to_add: maximum offset to add to positions during training
      when randomizing; this offset plus input length must still be less than
      max_len for all training examples.

  Returns:
    A Transformer language model as a layer that maps from a tensor of tokens
    to activations over a vocab set.
  
trax/models/research/configurable_transformer.py