code: def __init__(
      self,
      out_channels,
      embed_dim,
      num_heads,
      project_input=False,
      gated=False,
      downsample=False,
  ):
      super().__init__()
      self.attention = DownsampledMultiHeadAttention(
          out_channels,
          embed_dim,
          num_heads,
          dropout=0,
          bias=True,
          project_input=project_input,
          gated=gated,
          downsample=downsample,
      )
      self.in_proj_q = Linear(out_channels, embed_dim)
      self.in_proj_k = Linear(out_channels, embed_dim)
      self.in_proj_v = Linear(out_channels, embed_dim)
      self.ln = LayerNorm(out_channels)
  
imports: import logging
  import math
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import checkpoint_utils
  from fairseq.incremental_decoding_utils import with_incremental_state
  from fairseq.models import CompositeEncoder, FairseqDecoder, FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.modules import DownsampledMultiHeadAttention, FairseqDropout, GradMultiply, LayerNorm, LearnedPositionalEmbedding, LinearizedConvolution
label_desc: Use image node_id 1 to create a new SelfAttention object from inherited base classes: nn with example: obj = SelfAttention(out_channels, embed_dim, num_heads, project_input, gated, downsample)