content: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import math
  import numpy as np
  def gelu(x):
      return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
  class Norm(nn.Module):
      def __init__(self, d_model, eps = 1e-6):
          super().__init__()
      
          self.size = d_model
          
          # create two learnable parameters to calibrate normalisation
          self.alpha = nn.Parameter(torch.ones(self.size))
          self.bias = nn.Parameter(torch.zeros(self.size))
          
          self.eps = eps
      
      def forward(self, x):
          norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) \
          / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
          return norm
  
  def attention(q, k, v, d_k, mask=None, dropout=None):
      
      scores = torch.matmul(q, k.transpose(-2, -1)) /  math.sqrt(d_k)
      
      if mask is not None:
          mask = mask.unsqueeze(1)
          scores = scores.masked_fill(mask == 0, -1e9)
      
      scores = F.softmax(scores, dim=-1)
      
      if dropout is not None:
          scores = dropout(scores)
          
      output = torch.matmul(scores, v)
      return output
  
  class MultiHeadAttention(nn.Module):
      def __init__(self, heads, d_model, dropout = 0.1):
          super().__init__()
          
          self.d_model = d_model
          self.d_k = d_model // heads
          self.h = heads
          
          self.q_linear = 全连接层(d_model, d_model)
          self.v_linear = 全连接层(d_model, d_model)
          self.k_linear = 全连接层(d_model, d_model)
          
          self.dropout = nn.Dropout(dropout)
          self.out = 全连接层(d_model, d_model)
      
      def forward(self, q, k, v, mask=None):
          
          bs = q.size(0)
          
          # perform linear operation and split into N heads
          k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
          q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
          v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
          
          # transpose to get dimensions bs * N * sl * d_model
          k = k.transpose(1,2)
          q = q.transpose(1,2)
          v = v.transpose(1,2)
          
  
          # calculate attention using function we will define next
          scores = attention(q, k, v, self.d_k, mask, self.dropout)
          # concatenate heads and put through final linear layer
          concat = scores.transpose(1,2).contiguous()\
          .view(bs, -1, self.d_model)
          output = self.out(concat)
      
          return output
  
  class FeedForward(nn.Module):
      def __init__(self, d_model, d_ff=2048, dropout = 0.1):
          super().__init__() 
      
          # We set d_ff as a default to 2048
          self.linear_1 = 全连接层(d_model, d_ff)
          self.dropout = nn.Dropout(dropout)
          self.linear_2 = 全连接层(d_ff, d_model)
      
      def forward(self, x):
          x = self.dropout(gelu(self.linear_1(x)))
          x = self.linear_2(x)
          return x
  class 全连接层(nn.Module):
      def __init__(self,输入_接口, 输出_接口):
          super().__init__()
          np.random.seed(1)
          self.weight = nn.Parameter(torch.FloatTensor(np.random.uniform(-1/np.sqrt(输入_接口), 1/np.sqrt(输入_接口), (输入_接口, 输出_接口))))
          self.bias = nn.Parameter(torch.FloatTensor(np.random.uniform(-1/np.sqrt(输入_接口), 1/np.sqrt(输入_接口), 输出_接口)))
  
  
      def forward(self, x):
          输出=torch.matmul(x,self.weight)
          输出=输出+self.bias
          return 输出