code: def __init__(self, cfg_arch):
      super().__init__()
  
      if cfg_arch.embedding.embedding_dim == cfg_arch.hidden_size:
          output_size = cfg_arch.hidden_size
      else:
          output_size = cfg_arch.embedding.embedding_dim
  
      self.dense = torch.nn.Linear(
          cfg_arch.hidden_size, output_size, bias=cfg_arch.use_bias
      )
      self.nonlin = _get_nonlin_fn(cfg_arch.nonlin, use_gating=False)()
      self.norm = _get_norm_fn(cfg_arch.norm)(
          output_size, eps=cfg_arch.norm_eps
      )
  
imports: import torch
  import math
  from typing import Optional, Tuple
  from functools import partial
  from .embeddings import SinusoidalPositional, LearnablePositional, ScaledSinosoidal
  from .attention import get_attention_mechanism
label_desc: Use image node_id 1 to create a new PredictionHeadComponent object from inherited base classes: torch.nn with example: obj = PredictionHeadComponent(cfg_arch)