user: What would the C source look like for the assembly code provided here:

```asm
<exit_with_help>:
  endbr64
  sub    $0x8,%rsp
  lea    D4(%rip),%rdi
  call   <printf@plt>
  mov    $0x1,%edi
  call   <exit@plt>
  add    $0x8,%rsp
  ret
```

tool_calls: name: retrieve_data
arguments: tag: D4
type: string

tool: D4: Usage: svm-train [options] training_set_file [model_file]
options:
-s svm_type : set type of SVM (default 0)
	0 -- C-SVC		(multi-class classification)
	1 -- nu-SVC		(multi-class classification)
	2 -- one-class SVM
	3 -- epsilon-SVR	(regression)
	4 -- nu-SVR		(regression)
-t kernel_type : set type of kernel function (default 2)
	0 -- linear: u'*v
	1 -- polynomial: (gamma*u'*v + coef0)^degree
	2 -- radial basis function: exp(-gamma*|u-v|^2)
	3 -- sigmoid: tanh(gamma*u'*v + coef0)
	4 -- precomputed kernel (kernel values in training_set_file)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/num_features)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
-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: svm-train [options] training_set_file [model_file]\n"
         "options:\n"
         "-s svm_type : set type of SVM (default 0)\n"
         "	0 -- C-SVC		(multi-class classification)\n"
         "	1 -- nu-SVC		(multi-class classification)\n"
         "	2 -- one-class SVM\n"
         "	3 -- epsilon-SVR	(regression)\n"
         "	4 -- nu-SVR		(regression)\n"
         "-t kernel_type : set type of kernel function (default 2)\n"
         "	0 -- linear: u'*v\n"
         "	1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
         "	2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
         "	3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
         "	4 -- precomputed kernel (kernel values in training_set_file)\n"
         "-d degree : set degree in kernel function (default 3)\n"
         "-g gamma : set gamma in kernel function (default 1/num_features)\n"
         "-r coef0 : set coef0 in kernel function (default 0)\n"
         "-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
         "-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
         "-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
         "-m cachesize : set cache memory size in MB (default 100)\n"
         "-e epsilon : set tolerance of termination criterion (default 0.001)\n"
         "-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
         "-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
         "-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
         "-v n: n-fold cross validation mode\n"
         "-q : quiet mode (no outputs)\n"
         "-W weight_file: set weight file\n");
  exit(1);
}
```