code: def __init__(
      self,
      spec_dim,
      embedding_dim,
      attn_blocks=6,
      num_attn_heads=4,
  ):
      super().__init__()
      attn = []
      self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
      for a in range(attn_blocks):
          attn.append(AttentionBlock(embedding_dim, num_attn_heads))
      self.attn = nn.Sequential(*attn)
      self.dim = embedding_dim
  
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new ConditioningEncoder object from inherited base classes: nn with example: obj = ConditioningEncoder(spec_dim, embedding_dim, attn_blocks, num_attn_heads)