user: 提供的汇编代码用C语言表示会是什么样子:

```asm
<backprop>:
  endbr64
  push   %r15
  push   %r14
  push   %r13
  push   %r12
  push   %rbp
  push   %rbx
  lea    -0xa000(%rsp),%r11
L21:
  sub    $0x1000,%rsp
  orq    $0x0,(%rsp)
  cmp    %r11,%rsp
  jne    L21
  sub    $0xd18,%rsp
  mov    0xad50(%rsp),%r11
  mov    %rdi,0x18(%rsp)
  mov    %rsi,%r15
  lea    0x100(%rsp),%rbp
  lea    0x300(%rsp),%rbx
  mov    %rdx,(%rsp)
  lea    0x80(%rsp),%r14
  lea    0xe0(%rsp),%r13
  mov    %rcx,0x20(%rsp)
  lea    0xb00(%rsp),%r12
  mov    %r8,0x28(%rsp)
  mov    %r9,0x30(%rsp)
  mov    %fs:0x28,%rax
  mov    %rax,0xad08(%rsp)
  xor    %eax,%eax
  mov    0xad58(%rsp),%rax
  mov    %rax,0x10(%rsp)
  lea    0x4238(%r11),%rax
  mov    %rax,0x78(%rsp)
  lea    0x500(%rsp),%rax
  mov    %rax,0x48(%rsp)
  lea    0x700(%rsp),%rax
  mov    %rax,0x38(%rsp)
  lea    0xa0(%rsp),%rax
  mov    %rax,0x40(%rsp)
  lea    0xc0(%rsp),%rax
  mov    %rax,0x68(%rsp)
  lea    0xd00(%rsp),%rax
  mov    %rax,0x60(%rsp)
  lea    0x2d00(%rsp),%rax
  mov    %rax,0x50(%rsp)
  lea    0x900(%rsp),%rax
  mov    %rax,0x8(%rsp)
  lea    0x1300(%rsp),%rax
  mov    %rax,0x58(%rsp)
  nopl   (%rax)
L0:
  xor    %eax,%eax
  jmp    L43
  nopl   0x0(%rax)
L50:
  cmp    $0x3f,%rax
  je     L28
L16:
  add    $0x1,%rax
L43:
  movq   $0x0,0x0(%rbp,%rax,8)
  movq   $0x0,(%rbx,%rax,8)
  cmp    $0x2,%rax
  ja     L50
  movq   $0x0,(%r14,%rax,8)
  jmp    L16
  nopl   0x0(%rax,%rax,1)
L28:
  mov    0x18(%rsp),%rsi
  mov    0x20(%rsp),%rdi
  mov    %r11,%rcx
  mov    %rbp,%rdx
  mov    %r11,0x70(%rsp)
  call   <matrix_vector_product_with_bias_input_layer@plt>
  mov    0x48(%rsp),%rsi
  mov    $0x40,%edx
  mov    %rbp,%rdi
  call   <RELU@plt>
  mov    0x28(%rsp),%rdi
  mov    %rbp,%rcx
  mov    %rbx,%rdx
  mov    %r15,%rsi
  call   <matrix_vector_product_with_bias_second_layer@plt>
  mov    0x38(%rsp),%rsi
  mov    $0x40,%edx
  mov    %rbx,%rdi
  call   <RELU@plt>
  mov    (%rsp),%rsi
  mov    %rbx,%rcx
  mov    %r14,%rdx
  mov    0x30(%rsp),%rdi
  call   <matrix_vector_product_with_bias_output_layer@plt>
  mov    0x40(%rsp),%rsi
  mov    $0x3,%edx
  mov    %r14,%rdi
  call   <RELU@plt>
  mov    0x68(%rsp),%rdi
  mov    %r14,%rsi
  call   <soft_max@plt>
  mov    0x40(%rsp),%rcx
  mov    0x10(%rsp),%rsi
  mov    %r13,%rdx
  mov    0x68(%rsp),%rdi
  call   <take_difference@plt>
  mov    0x60(%rsp),%rdi
  mov    %rbx,%rdx
  mov    %r13,%rsi
  call   <get_delta_matrix_weights3@plt>
  mov    0x38(%rsp),%rcx
  mov    %r12,%rdx
  mov    %r13,%rsi
  mov    (%rsp),%rdi
  call   <get_oracle_activations2@plt>
  mov    0x50(%rsp),%rdi
  mov    %rbp,%rdx
  mov    %r12,%rsi
  call   <get_delta_matrix_weights2@plt>
  mov    0x48(%rsp),%rcx
  mov    %r12,%rsi
  mov    %r15,%rdi
  mov    0x8(%rsp),%rdx
  call   <get_oracle_activations1@plt>
  mov    0x70(%rsp),%rdx
  mov    0x8(%rsp),%rsi
  mov    0x58(%rsp),%rdi
  call   <get_delta_matrix_weights1@plt>
  push   %r13
  mov    %r15,%rsi
  push   %r12
  push   0x18(%rsp)
  push   0x48(%rsp)
  push   0x48(%rsp)
  push   0x48(%rsp)
  mov    0x90(%rsp),%r9
  mov    0x80(%rsp),%r8
  mov    0x88(%rsp),%rcx
  mov    0x30(%rsp),%rdx
  mov    0x48(%rsp),%rdi
  call   <update_weights@plt>
  mov    0xa0(%rsp),%r11
  addq   $0x18,0x40(%rsp)
  add    $0x30,%rsp
  add    $0x68,%r11
  cmp    0x78(%rsp),%r11
  jne    L0
  mov    0xad08(%rsp),%rax
  sub    %fs:0x28,%rax
  jne    L42
  add    $0xad18,%rsp
  pop    %rbx
  pop    %rbp
  pop    %r12
  pop    %r13
  pop    %r14
  pop    %r15
  ret
L42:
  call   <__stack_chk_fail@plt>
```

assistant: ```c
void backprop(double weights1[13 * 64], double weights2[64 * 64], double weights3[64 * 3], double biases1[64], double biases2[64], double biases3[3], double training_data[163 * 13], double training_targets[163 * 3]) {
  int i, j;
  double activations1[64];
  double activations2[64];
  double activations3[3];
  double dactivations1[64];
  double dactivations2[64];
  double dactivations3[3];
  double net_outputs[3];
  double output_difference[3];
  double delta_weights1[13 * 64];
  double delta_weights2[64 * 64];
  double delta_weights3[64 * 3];
  double oracle_activations1[64];
  double oracle_activations2[64];
  for (i = 0; i < 163; i++) {
    for (j = 0; j < 64; j++) {
      activations1[j] = (double)0.0;
      activations2[j] = (double)0.0;
      if (j < 3) {
        activations3[j] = (double)0.0;
      }
    }
    matrix_vector_product_with_bias_input_layer(biases1, weights1, activations1, &training_data[i * 13]);
    RELU(activations1, dactivations1, 64);
    matrix_vector_product_with_bias_second_layer(biases2, weights2, activations2, activations1);
    RELU(activations2, dactivations2, 64);
    matrix_vector_product_with_bias_output_layer(biases3, weights3, activations3, activations2);
    RELU(activations3, dactivations3, 3);
    soft_max(net_outputs, activations3);
    take_difference(net_outputs, &training_targets[i * 3], output_difference, dactivations3);
    get_delta_matrix_weights3(delta_weights3, output_difference, activations2);
    get_oracle_activations2(weights3, output_difference, oracle_activations2, dactivations2);
    get_delta_matrix_weights2(delta_weights2, oracle_activations2, activations1);
    get_oracle_activations1(weights2, oracle_activations2, oracle_activations1, dactivations1);
    get_delta_matrix_weights1(delta_weights1, oracle_activations1, &training_data[i * 13]);
    update_weights(weights1, weights2, weights3, delta_weights1, delta_weights2, delta_weights3, biases1, biases2, biases3, oracle_activations1, oracle_activations2, output_difference);
  }
  return;
}
```