user: 请将以下的汇编代码转换成C语言代码：

```asm
<exit_with_help>:
  endbr64
  push   %rbp
  mov    %rsp,%rbp
  lea    D4(%rip),%rax
  mov    %rax,%rdi
  call   <printf@plt>
  mov    $0x1,%edi
  call   <exit@plt>
  nop
  pop    %rbp
  ret
```

tool_calls: name: extract_symbolic_data
arguments: label: D4
type_guessed: string

tool: D4: Usage: train [options] training_set_file [model_file]
options:
-s type : set type of solver (default 1)
	 0 -- L2-regularized logistic regression (primal)
	 1 -- L2-regularized L2-loss support vector classification (dual)
	 2 -- L2-regularized L2-loss support vector classification (primal)
	 3 -- L2-regularized L1-loss support vector classification (dual)
	 4 -- multi-class support vector classification by Crammer and Singer
	 5 -- L1-regularized L2-loss support vector classification
	 6 -- L1-regularized logistic regression
	 7 -- L2-regularized logistic regression (dual)
	11 -- L2-regularized L2-loss epsilon support vector regression (primal)
	12 -- L2-regularized L2-loss epsilon support vector regression (dual)
	13 -- L2-regularized L1-loss epsilon support vector regression (dual)
-c cost : set the parameter C (default 1)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-e epsilon : set tolerance of termination criterion
	-s 0 and 2
		|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,
		where f is the primal function and pos/neg are # of
		positive/negative data (default 0.01)
	-s 11
		|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)
	-s 1, 3, 4, and 7
		Dual maximal violation <= eps; similar to libsvm (default 0.1)
	-s 5 and 6
		|f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,
		where f is the primal function (default 0.01)
	-s 12 and 13
		|f'(alpha)|_1 <= eps |f'(alpha0)|,
		where f is the dual function (default 0.1)
-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
-wi weight: weights adjust the parameter C of different classes (see README for details)
-v n: n-fold cross validation mode
-q : quiet mode (no outputs)
-W weight_file: set weight file


assistant: ```c
void exit_with_help() {
  printf("Usage: train [options] training_set_file [model_file]\n"
         "options:\n"
         "-s type : set type of solver (default 1)\n"
         "	 0 -- L2-regularized logistic regression (primal)\n"
         "	 1 -- L2-regularized L2-loss support vector classification (dual)\n"
         "	 2 -- L2-regularized L2-loss support vector classification (primal)\n"
         "	 3 -- L2-regularized L1-loss support vector classification (dual)\n"
         "	 4 -- multi-class support vector classification by Crammer and Singer\n"
         "	 5 -- L1-regularized L2-loss support vector classification\n"
         "	 6 -- L1-regularized logistic regression\n"
         "	 7 -- L2-regularized logistic regression (dual)\n"
         "	11 -- L2-regularized L2-loss epsilon support vector regression (primal)\n"
         "	12 -- L2-regularized L2-loss epsilon support vector regression (dual)\n"
         "	13 -- L2-regularized L1-loss epsilon support vector regression (dual)\n"
         "-c cost : set the parameter C (default 1)\n"
         "-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
         "-e epsilon : set tolerance of termination criterion\n"
         "	-s 0 and 2\n"
         "		|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,\n"
         "		where f is the primal function and pos/neg are # of\n"
         "		positive/negative data (default 0.01)\n"
         "	-s 11\n"
         "		|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)\n"
         "	-s 1, 3, 4, and 7\n"
         "		Dual maximal violation <= eps; similar to libsvm (default 0.1)\n"
         "	-s 5 and 6\n"
         "		|f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,\n"
         "		where f is the primal function (default 0.01)\n"
         "	-s 12 and 13\n"
         "		|f'(alpha)|_1 <= eps |f'(alpha0)|,\n"
         "		where f is the dual function (default 0.1)\n"
         "-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)\n"
         "-wi weight: weights adjust the parameter C of different classes (see README for details)\n"
         "-v n: n-fold cross validation mode\n"
         "-q : quiet mode (no outputs)\n"
         "-W weight_file: set weight file\n");
  exit(1);
}
```