class_docstr: Block to perform pre/post processing on layer inputs.
  The processing steps are determined by the sequence argument, which can contain one of the three operations:
  n: layer normalization
  r: residual connection
  d: dropout
code: def __init__(
      self,
      sequence: str,
      dropout: float,
      num_hidden: int = 0,
      dtype: Optional[pt.dtype] = None,
      clamp_to_dtype: bool = False,
  ) -> None:
      super().__init__()
      self.sequence = sequence
      self.clamp_to_dtype = clamp_to_dtype
      self.layer_norm = None
      if "n" in sequence:
          # do not use Apex' FusedLayerNorm because of
          # https://github.com/huggingface/transformers/issues/9377
          self.layer_norm = pt.nn.LayerNorm(
              num_hidden, eps=1e-06, dtype=dtype
          )
      self.dropout = dropout
      self.drop = pt.nn.Dropout(p=dropout)
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  import torch
  import sockeye.layers
  from sockeye import constants
  from .None import config
label_desc: Use image node_id 1 to create a new TransformerProcessBlock object from inherited base classes: pt.nn with example: obj = TransformerProcessBlock(sequence, dropout, num_hidden, dtype, clamp_to_dtype)