def forward(
            self,
            query: Tensor,
            key: Tensor,
            value: Tensor,
            key_padding_mask: Optional[Tensor] = None,
            need_weights: bool = True,
            attn_mask: Optional[Tensor] = None,
            average_attn_weights: bool = True,
            cache=None,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Args:
            query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
                or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
                :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
                Queries are compared against key-value pairs to produce the output.
                See "Attention Is All You Need" for more details.
            key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
                or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
                :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
                See "Attention Is All You Need" for more details.
            value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
                ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
                sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
                See "Attention Is All You Need" for more details.
            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
                to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
                Binary and byte masks are supported.
                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
                the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
                Default: ``True``.
            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
                the attention weight.
            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
                heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
                effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)

        Outputs:
            - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
              :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
              where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
              embedding dimension ``embed_dim``.
            - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
              returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
              head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.

            .. note::
                `batch_first` argument is ignored for unbatched inputs.
        """
        is_batched = query.dim() == 3
        if key_padding_mask is not None:
            _kpm_dtype = key_padding_mask.dtype
            if _kpm_dtype != torch.bool and not torch.is_floating_point(
                    key_padding_mask
            ):
                raise AssertionError(
                    "only bool and floating types of key_padding_mask are supported"
                )
        why_not_fast_path = ""
        if not is_batched:
            why_not_fast_path = (
                f"input not batched; expected query.dim() of 3 but got {query.dim()}"
            )
        elif query is not key or key is not value:
            # When lifting this restriction, don't forget to either
            # enforce that the dtypes all match or test cases where
            # they don't!
            why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
        elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype:
            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
        elif (
                self.in_proj_weight is not None and query.dtype != self.in_proj_weight.dtype
        ):
            # this case will fail anyway, but at least they'll get a useful error message.
            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
        elif self.training:
            why_not_fast_path = "training is enabled"
        elif not self.batch_first:
            why_not_fast_path = "batch_first was not True"
        elif self.bias_k is not None:
            why_not_fast_path = "self.bias_k was not None"
        elif self.bias_v is not None:
            why_not_fast_path = "self.bias_v was not None"
        elif self.dropout:
            why_not_fast_path = f"dropout was {self.dropout}, required zero"
        elif self.add_zero_attn:
            why_not_fast_path = "add_zero_attn was enabled"
        elif not self._qkv_same_embed_dim:
            why_not_fast_path = "_qkv_same_embed_dim was not True"
        elif attn_mask is not None:
            why_not_fast_path = "attn_mask was not None"
        elif query.is_nested and key_padding_mask is not None:
            why_not_fast_path = (
                "key_padding_mask is not supported with NestedTensor input"
            )
        elif self.num_heads % 2 == 1:
            why_not_fast_path = "num_heads is odd"
        elif torch.is_autocast_enabled():
            why_not_fast_path = "autocast is enabled"

        if not why_not_fast_path:
            tensor_args = (
                query,
                key,
                value,
                self.in_proj_weight,
                self.in_proj_bias,
                self.out_proj.weight,
                self.out_proj.bias,
            )
            # We have to use list comprehensions below because TorchScript does not support
            # generator expressions.
            if torch.overrides.has_torch_function(tensor_args):
                why_not_fast_path = "some Tensor argument has_torch_function"
            elif not all(
                    [
                        (x is None or x.is_cuda or "cpu" in str(x.device))
                        for x in tensor_args
                    ]
            ):
                why_not_fast_path = "some Tensor argument is neither CUDA nor CPU"
            elif torch.is_grad_enabled() and any(
                    [x is not None and x.requires_grad for x in tensor_args]
            ):
                why_not_fast_path = (
                    "grad is enabled and at least one of query or the "
                    "input/output projection weights or biases requires_grad"
                )
            if not why_not_fast_path:
                return torch._native_multi_head_attention(
                    query,
                    key,
                    value,
                    self.embed_dim,
                    self.num_heads,
                    self.in_proj_weight,
                    self.in_proj_bias,
                    self.out_proj.weight,
                    self.out_proj.bias,
                    key_padding_mask if key_padding_mask is not None else attn_mask,
                    need_weights,
                    average_attn_weights,
                    1
                    if key_padding_mask is not None
                    else 0
                    if attn_mask is not None
                    else None,
                )

        any_nested = query.is_nested or key.is_nested or value.is_nested
        assert not any_nested, (
                "MultiheadAttention does not support NestedTensor outside of its fast path. "
                + f"The fast path was not hit because {why_not_fast_path}"
        )

        if self.batch_first and is_batched:
            # make sure that the transpose op does not affect the "is" property
            if key is value:
                if query is key:
                    query = key = value = query.transpose(1, 0)
                else:
                    query, key = [x.transpose(1, 0) for x in (query, key)]
                    value = key
            else:
                query, key, value = [x.transpose(1, 0) for x in (query, key, value)]

        if not self._qkv_same_embed_dim:
            attn_output, attn_output_weights = F.multi_head_attention_forward(
                query,
                key,
                value,
                self.embed_dim,
                self.num_heads,
                self.in_proj_weight,
                self.in_proj_bias,
                self.bias_k,
                self.bias_v,
                self.add_zero_attn,
                self.dropout,
                self.out_proj.weight,
                self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=attn_mask,
                use_separate_proj_weight=True,
                q_proj_weight=self.q_proj_weight,
                k_proj_weight=self.k_proj_weight,
                v_proj_weight=self.v_proj_weight,
                average_attn_weights=average_attn_weights,
                cache=cache,
            )
        else:
            attn_output, attn_output_weights = F.multi_head_attention_forward(
                query,
                key,
                value,
                self.embed_dim,
                self.num_heads,
                self.in_proj_weight,
                self.in_proj_bias,
                self.bias_k,
                self.bias_v,
                self.add_zero_attn,
                self.dropout,
                self.out_proj.weight,
                self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=attn_mask,
                average_attn_weights=average_attn_weights,
                cache=cache,
            )
        if self.batch_first and is_batched:
            return attn_output.transpose(1, 0), attn_output_weights
        else:
            return attn_output, attn_output_weights

        Args:
            query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
                or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
                :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
                Queries are compared against key-value pairs to produce the output.
                See "Attention Is All You Need" for more details.
            key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
                or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
                :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
                See "Attention Is All You Need" for more details.
            value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
                ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
                sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
                See "Attention Is All You Need" for more details.
            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
                to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
                Binary and byte masks are supported.
                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
                the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
                Default: ``True``.
            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
                the attention weight.
            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
                heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
                effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)

        Outputs:
            - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
              :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
              where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
              embedding dimension ``embed_dim``.
            - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
              returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
              head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.

            .. note::
                `batch_first` argument is ignored for unbatched inputs.
        
parrots/t2s_utils.py