code: def __init__(
      self,
      q_len,
      hidden_size,
      n_heads,
      d_k=None,
      d_v=None,
      linear_hidden_size=256,
      store_attn=False,
      norm="BatchNorm",
      attn_dropout=0,
      dropout=0.0,
      bias=True,
      activation="gelu",
      res_attention=False,
      pre_norm=False,
  ):
      super().__init__()
      assert (
          not hidden_size % n_heads
      ), f"hidden_size ({hidden_size}) must be divisible by n_heads ({n_heads})"
      d_k = hidden_size // n_heads if d_k is None else d_k
      d_v = hidden_size // n_heads if d_v is None else d_v
  
      # Multi-Head attention
      self.res_attention = res_attention
      self.self_attn = _MultiheadAttention(
          hidden_size,
          n_heads,
          d_k,
          d_v,
          attn_dropout=attn_dropout,
          proj_dropout=dropout,
          res_attention=res_attention,
      )
  
      # Add & Norm
      self.dropout_attn = nn.Dropout(dropout)
      if "batch" in norm.lower():
          self.norm_attn = nn.Sequential(
              Transpose(1, 2),
              nn.BatchNorm1d(hidden_size),
              Transpose(1, 2),
          )
      else:
          self.norm_attn = nn.LayerNorm(hidden_size)
  
      # Position-wise Feed-Forward
      self.ff = nn.Sequential(
          nn.Linear(hidden_size, linear_hidden_size, bias=bias),
          get_activation_fn(activation),
          nn.Dropout(dropout),
          nn.Linear(linear_hidden_size, hidden_size, bias=bias),
      )
  
      # Add & Norm
      self.dropout_ffn = nn.Dropout(dropout)
      if "batch" in norm.lower():
          self.norm_ffn = nn.Sequential(
              Transpose(1, 2),
              nn.BatchNorm1d(hidden_size),
              Transpose(1, 2),
          )
      else:
          self.norm_ffn = nn.LayerNorm(hidden_size)
  
      self.pre_norm = pre_norm
      self.store_attn = store_attn
  
imports: import math
  import numpy
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 1 to create a new TSTEncoderLayer object from inherited base classes: nn with example: obj = TSTEncoderLayer(q_len, hidden_size, n_heads, d_k, d_v, linear_hidden_size, store_attn, norm, attn_dropout, dropout, bias, activation, res_attention, pre_norm)