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 forward(self, x):
      x = self.input_drop(x)
  
      if self.training and self.noise_level > 0:
          noise = torch.randn_like(x).to(x)
          x = x + noise * self.noise_level
  
      x = x.permute(
          1, 0, 2
      ).contiguous()  # the first dim need to be sequence
      x = self.pe(x)
  
      x = self.input_proj(x)
      out = self.encoder(x)
  
      return out[-1]
  
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 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(x) and returns: out