class_docstr: Transformer Model
  
  Args:
      input_size (int): input size (# features)
      hidden_size (int): hidden size
      num_layers (int): number of transformer layers
      num_heads (int): number of heads in transformer
      dropout (float): dropout rate
      input_drop (float): input dropout for data augmentation
      noise_level (float): add gaussian noise to input for data augmentation
code: def __init__(
      self,
      input_size=16,
      hidden_size=64,
      num_layers=2,
      num_heads=2,
      dropout=0.0,
      input_drop=0.0,
      noise_level=0.0,
      **kwargs,
  ):
      super().__init__()
  
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.num_layers = num_layers
      self.num_heads = num_heads
      self.noise_level = noise_level
  
      self.input_drop = nn.Dropout(input_drop)
  
      self.input_proj = nn.Linear(input_size, hidden_size)
  
      self.pe = PositionalEncoding(input_size, dropout)
      layer = nn.TransformerEncoderLayer(
          nhead=num_heads,
          dropout=dropout,
          d_model=hidden_size,
          dim_feedforward=hidden_size * 4,
      )
      self.encoder = nn.TransformerEncoder(layer, num_layers=num_layers)
  
      self.output_size = hidden_size
  
imports: import os
  import copy
  import math
  import json
  import collections
  import numpy
  import pandas
  import torch
  import torch.nn
  import torch.optim
  import torch.nn.functional
  from tqdm import tqdm
  from qlib.utils import get_or_create_path
  from qlib.log import get_module_logger
  from qlib.model.base import Model
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(input_size, hidden_size, num_layers, num_heads, dropout, input_drop, noise_level)