Pretty-print dataclasses (#76810)
Unfortunately the built-in pprint module support pretty-print of dataclasses only from python 3.10. The code that I wrote in method `__str__` of OpInfo should do the same job and should also work for any dataclass. For now I've put it there but we can create a function and put it somewhere where is accessible also for other dataclasses. Also the max width (80) is now hardcode but it would ideally be the parameter of the function.
when you call print on an OpInfo you get:
```
OpInfo(name = '__getitem__',
ref = None,
aliases = (),
variant_test_name = '',
op = <slot wrapper '__getitem__' of 'torch._C._TensorBase' objects>,
method_variant = <slot wrapper '__getitem__' of 'torch._C._TensorBase' objects>,
inplace_variant = None,
skips = (<torch.testing._internal.common_methods_invocations.DecorateInfo object at 0x7f463acbca90>,
<torch.testing._internal.common_methods_invocations.DecorateInfo object at 0x7f463acbcae0>),
decorators = (<torch.testing._internal.common_methods_invocations.DecorateInfo object at 0x7f463acbca90>,
<torch.testing._internal.common_methods_invocations.DecorateInfo object at 0x7f463acbcae0>),
sample_inputs_func = <function sample_inputs_getitem at 0x7f463acc6af0>,
reference_inputs_func = None,
error_inputs_func = None,
sample_inputs_sparse_coo_func = <function _DecoratorContextManager.__call__.<locals>.decorate_context at 0x7f463acc6b80>,
sample_inputs_sparse_csr_func = <function _DecoratorContextManager.__call__.<locals>.decorate_context at 0x7f463acc6c10>,
dtypes = {torch.int16,
torch.float64,
torch.int32,
torch.int64,
torch.complex64,
torch.float16,
torch.bfloat16,
torch.uint8,
torch.complex128,
torch.bool,
torch.float32,
torch.int8},
dtypesIfCUDA = {torch.int16,
torch.float64,
torch.int32,
torch.int64,
torch.complex64,
torch.float16,
torch.bfloat16,
torch.uint8,
torch.complex128,
torch.bool,
torch.float32,
torch.int8},
dtypesIfROCM = {torch.int16,
torch.float64,
torch.int32,
torch.int64,
torch.complex64,
torch.float16,
torch.bfloat16,
torch.uint8,
torch.complex128,
torch.bool,
torch.float32,
torch.int8},
backward_dtypes = {torch.int16,
torch.float64,
torch.int32,
torch.int64,
torch.complex64,
torch.float16,
torch.bfloat16,
torch.uint8,
torch.complex128,
torch.bool,
torch.float32,
torch.int8},
backward_dtypesIfCUDA = {torch.int16,
torch.float64,
torch.int32,
torch.int64,
torch.complex64,
torch.float16,
torch.bfloat16,
torch.uint8,
torch.complex128,
torch.bool,
torch.float32,
torch.int8},
backward_dtypesIfROCM = {torch.int16,
torch.float64,
torch.int32,
torch.int64,
torch.complex64,
torch.float16,
torch.bfloat16,
torch.uint8,
torch.complex128,
torch.bool,
torch.float32,
torch.int8},
supports_out = False,
supports_autograd = True,
supports_gradgrad = True,
supports_fwgrad_bwgrad = True,
supports_inplace_autograd = False,
supports_forward_ad = True,
gradcheck_wrapper = <function OpInfo.<lambda> at 0x7f463a7a40d0>,
check_batched_grad = True,
check_batched_gradgrad = True,
check_batched_forward_grad = True,
check_inplace_batched_forward_grad = True,
gradcheck_nondet_tol = 0.0,
gradcheck_fast_mode = None,
aten_name = '__getitem__',
decomp_aten_name = None,
aten_backward_name = None,
assert_autodiffed = False,
autodiff_nonfusible_nodes = ['aten::__getitem__'],
autodiff_fusible_nodes = [],
supports_sparse = False,
supports_scripting = False,
supports_sparse_csr = False,
test_conjugated_samples = True,
test_neg_view = True,
assert_jit_shape_analysis = False,
supports_expanded_weight = False)
```
cc @ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76810
Approved by: https://github.com/ezyang
