error error error full of this

[shreesha345]

```Working` with z of shape (1, 256, 16, 16) = 65536 dimensions.
loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth
VQLPIPSWithDiscriminator running with hinge loss.
Restored from models/vqgan_imagenet_f16_16384.ckpt
Using device: cuda:0
Optimising using: Adam
Using text prompts: ['underwater city']
Using seed: 1698681138380486500
0/? [00:00<?, ?it/s]
Oops: runtime error: solve: MAGMA library not found in compilation. Please rebuild with MAGMA.
Try reducing --num-cuts to save memory

RuntimeError Traceback (most recent call last)
/tmp/ipykernel_58/2225298613.py in
21 settings = clipit.apply_settings()
22 clipit.do_init(settings)
---> 23 clipit.do_run(settings)

/kaggle/working/clipit/clipit.py in do_run(args)
997 print("Oops: runtime error: ", e)
998 print("Try reducing --num-cuts to save memory")
--> 999 raise e
1000 except KeyboardInterrupt:
1001 pass

/kaggle/working/clipit/clipit.py in do_run(args)
989 while True:
990 try:
--> 991 train(args, cur_iteration)
992 if cur_iteration == args.iterations:
993 break

/kaggle/working/clipit/clipit.py in train(args, cur_it)
902
903 for i in range(args.batches):
--> 904 lossAll = ascend_txt(args)
905
906 if i == 0 and cur_it % args.save_every == 0:

/kaggle/working/clipit/clipit.py in ascend_txt(args)
723 for cutoutSize in cutoutsTable:
724 make_cutouts = cutoutsTable[cutoutSize]
--> 725 cur_cutouts[cutoutSize] = make_cutouts(out)
726
727 if args.spot_prompts:

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []

/kaggle/working/clipit/clipit.py in forward(self, input, spot)
352 # TF.to_pil_image(batch[j_wide].cpu()).save(f"cached_im_{cur_iteration:02d}{j_wide:02d}{spot}.png")
353 else:
--> 354 batch1, transforms1 = self.augs_zoom(torch.cat(cutouts[:self.cutn_zoom], dim=0))
355 batch2, transforms2 = self.augs_wide(torch.cat(cutouts[self.cutn_zoom:], dim=0))
356 # print(batch1.shape, batch2.shape)

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input
141

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.7/site-packages/kornia/augmentation/augmentation.py in forward(self, input, params, return_transform)
1141 input_pad = self.compute_padding(input_temp.shape)
1142 _input = self.precrop_padding(input_temp, input_pad) # type: ignore
-> 1143 out = super().forward(_input, params, return_transform)
1144
1145 # Update the actual input size for inverse

/opt/conda/lib/python3.7/site-packages/kornia/augmentation/base.py in forward(self, input, params, return_transform)
243
244 self._params = params
--> 245 output = self.apply_func(in_tensor, in_transform, self._params, return_transform)
246 return _transform_output_shape(output, ori_shape) if self.keepdim else output
247

/opt/conda/lib/python3.7/site-packages/kornia/augmentation/base.py in apply_func(self, in_tensor, in_transform, params, return_transform)
202 # if all data needs to be augmented
203 elif torch.sum(to_apply) == len(to_apply):
--> 204 trans_matrix = self.compute_transformation(in_tensor, params)
205 output = self.apply_transform(in_tensor, params, trans_matrix)
206 else:

/opt/conda/lib/python3.7/site-packages/kornia/augmentation/augmentation.py in compute_transformation(self, input, params)
1063
1064 def compute_transformation(self, input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
-> 1065 transform: torch.Tensor = get_perspective_transform(params['src'].to(input), params['dst'].to(input))
1066 return transform
1067

/opt/conda/lib/python3.7/site-packages/kornia/geometry/transform/imgwarp.py in get_perspective_transform(src, dst)
281
282 # solve the system Ax = b
--> 283 X, LU = _torch_solve_cast(b, A)
284
285 # create variable to return

/opt/conda/lib/python3.7/site-packages/kornia/utils/helpers.py in _torch_solve_cast(input, A)
94 dtype = torch.float32
95
---> 96 out1, out2 = torch.solve(input.to(dtype), A.to(dtype))
97
98 return (out1.to(input.dtype), out2.to(input.dtype))

RuntimeError: solve: MAGMA library not found in compilation. Please rebuild with MAGMA.```

here is the kaggle code please fix it 😭
https://www.kaggle.com/shreeshaaithal/notebookf22d408364