class_docstr: A transformer model.
  
  User is able to modify the attributes as needed. The architecture
  is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer,
  Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and
  Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information
  Processing Systems, pages 6000-6010.
  
  Args:
      d_model: the number of expected features in the encoder/decoder inputs (default=512).
      nhead: the number of heads in the multiheadattention models (default=8).
      num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).
      num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6).
      dim_feedforward: the dimension of the feedforward network model (default=2048).
      dropout: the dropout value (default=0.1).
      activation: the activation function of encoder/decoder intermediate layer, can be a string
          ("relu" or "gelu") or a unary callable. Default: relu
      custom_encoder: custom encoder (default=None).
      custom_decoder: custom decoder (default=None).
      layer_norm_eps: the eps value in layer normalization components (default=1e-5).
      batch_first: If ``True``, then the input and output tensors are provided
          as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
      norm_first: if ``True``, encoder and decoder layers will perform LayerNorms before
          other attention and feedforward operations, otherwise after. Default: ``False`` (after).
      bias: If set to ``False``, ``Linear`` and ``LayerNorm`` layers will not learn an additive
          bias. Default: ``True``.
  
  Examples::
      >>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
      >>> src = torch.rand((10, 32, 512))
      >>> tgt = torch.rand((20, 32, 512))
      >>> out = transformer_model(src, tgt)
  
  Note: A full example to apply nn.Transformer module for the word language model is available in
  https://github.com/pytorch/examples/tree/master/word_language_model
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docstr: Take in and process masked source/target sequences.
  
  Args:
      src: the sequence to the encoder (required).
      tgt: the sequence to the decoder (required).
      src_mask: the additive mask for the src sequence (optional).
      tgt_mask: the additive mask for the tgt sequence (optional).
      memory_mask: the additive mask for the encoder output (optional).
      src_key_padding_mask: the Tensor mask for src keys per batch (optional).
      tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional).
      memory_key_padding_mask: the Tensor mask for memory keys per batch (optional).
      src_is_causal: If specified, applies a causal mask as ``src_mask``.
          Default: ``None``; try to detect a causal mask.
          Warning:
          ``src_is_causal`` provides a hint that ``src_mask`` is
          the causal mask. Providing incorrect hints can result in
          incorrect execution, including forward and backward
          compatibility.
      tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``.
          Default: ``None``; try to detect a causal mask.
          Warning:
          ``tgt_is_causal`` provides a hint that ``tgt_mask`` is
          the causal mask. Providing incorrect hints can result in
          incorrect execution, including forward and backward
          compatibility.
      memory_is_causal: If specified, applies a causal mask as
          ``memory_mask``.
          Default: ``False``.
          Warning:
          ``memory_is_causal`` provides a hint that
          ``memory_mask`` is the causal mask. Providing incorrect
          hints can result in incorrect execution, including
          forward and backward compatibility.
  
  Shape:
      - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or
        `(N, S, E)` if `batch_first=True`.
      - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
        `(N, T, E)` if `batch_first=True`.
      - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`.
      - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`.
      - memory_mask: :math:`(T, S)`.
      - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
      - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`.
      - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
  
      Note: [src/tgt/memory]_mask ensures that position i is allowed to attend the unmasked
      positions. If a BoolTensor is provided, positions with ``True``
      are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
      is provided, it will be added to the attention weight.
      [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by
      the attention. If a BoolTensor is provided, the positions with the
      value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
  
      - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
        `(N, T, E)` if `batch_first=True`.
  
      Note: Due to the multi-head attention architecture in the transformer model,
      the output sequence length of a transformer is same as the input sequence
      (i.e. target) length of the decoder.
  
      where S is the source sequence length, T is the target sequence length, N is the
      batch size, E is the feature number
  
  Examples:
      >>> # xdoctest: +SKIP
      >>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)
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code: def forward(
      self,
      src: Tensor,
      tgt: Tensor,
      src_mask: Optional[Tensor] = None,
      tgt_mask: Optional[Tensor] = None,
      memory_mask: Optional[Tensor] = None,
      src_key_padding_mask: Optional[Tensor] = None,
      tgt_key_padding_mask: Optional[Tensor] = None,
      memory_key_padding_mask: Optional[Tensor] = None,
      src_is_causal: Optional[bool] = None,
      tgt_is_causal: Optional[bool] = None,
      memory_is_causal: bool = False,
  ) -> Tensor:
      r"""Take in and process masked source/target sequences.
  
      Args:
          src: the sequence to the encoder (required).
          tgt: the sequence to the decoder (required).
          src_mask: the additive mask for the src sequence (optional).
          tgt_mask: the additive mask for the tgt sequence (optional).
          memory_mask: the additive mask for the encoder output (optional).
          src_key_padding_mask: the Tensor mask for src keys per batch (optional).
          tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional).
          memory_key_padding_mask: the Tensor mask for memory keys per batch (optional).
          src_is_causal: If specified, applies a causal mask as ``src_mask``.
              Default: ``None``; try to detect a causal mask.
              Warning:
              ``src_is_causal`` provides a hint that ``src_mask`` is
              the causal mask. Providing incorrect hints can result in
              incorrect execution, including forward and backward
              compatibility.
          tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``.
              Default: ``None``; try to detect a causal mask.
              Warning:
              ``tgt_is_causal`` provides a hint that ``tgt_mask`` is
              the causal mask. Providing incorrect hints can result in
              incorrect execution, including forward and backward
              compatibility.
          memory_is_causal: If specified, applies a causal mask as
              ``memory_mask``.
              Default: ``False``.
              Warning:
              ``memory_is_causal`` provides a hint that
              ``memory_mask`` is the causal mask. Providing incorrect
              hints can result in incorrect execution, including
              forward and backward compatibility.
  
      Shape:
          - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or
            `(N, S, E)` if `batch_first=True`.
          - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
            `(N, T, E)` if `batch_first=True`.
          - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`.
          - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`.
          - memory_mask: :math:`(T, S)`.
          - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
          - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`.
          - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
  
          Note: [src/tgt/memory]_mask ensures that position i is allowed to attend the unmasked
          positions. If a BoolTensor is provided, positions with ``True``
          are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
          is provided, it will be added to the attention weight.
          [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by
          the attention. If a BoolTensor is provided, the positions with the
          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
  
          - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
            `(N, T, E)` if `batch_first=True`.
  
          Note: Due to the multi-head attention architecture in the transformer model,
          the output sequence length of a transformer is same as the input sequence
          (i.e. target) length of the decoder.
  
          where S is the source sequence length, T is the target sequence length, N is the
          batch size, E is the feature number
  
      Examples:
          >>> # xdoctest: +SKIP
          >>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)
      """
      is_batched = src.dim() == 3
      if (
          not self.batch_first
          and src.size(1) != tgt.size(1)
          and is_batched
      ):
          raise RuntimeError(
              "the batch number of src and tgt must be equal"
          )
      elif (
          self.batch_first and src.size(0) != tgt.size(0) and is_batched
      ):
          raise RuntimeError(
              "the batch number of src and tgt must be equal"
          )
  
      if src.size(-1) != self.d_model or tgt.size(-1) != self.d_model:
          raise RuntimeError(
              "the feature number of src and tgt must be equal to d_model"
          )
  
      memory = self.encoder(
          src,
          mask=src_mask,
          src_key_padding_mask=src_key_padding_mask,
          is_causal=src_is_causal,
      )
      output = self.decoder(
          tgt,
          memory,
          tgt_mask=tgt_mask,
          memory_mask=memory_mask,
          tgt_key_padding_mask=tgt_key_padding_mask,
          memory_key_padding_mask=memory_key_padding_mask,
          tgt_is_causal=tgt_is_causal,
          memory_is_causal=memory_is_causal,
      )
      return output
  
imports: import copy
  from typing import Optional, Any, Union, Callable
  import torch
  import warnings
  from torch import Tensor
  from ..None import functional
  from .module import Module
  from .activation import MultiheadAttention
  from .container import ModuleList
  from ..init import xavier_uniform_
  from .dropout import Dropout
  from .linear import Linear
  from .normalization import LayerNorm
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(src, tgt, src_mask, tgt_mask, memory_mask, src_key_padding_mask, tgt_key_padding_mask, memory_key_padding_mask, src_is_causal, tgt_is_causal, memory_is_causal) and returns: output