Implement max_norm argument for torch.nn.functional.embedding

[IvanYashchuk]

🚀 Feature

At the moment, the max_norm argument raises a NotImplementedError in Thunder:

lightning-thunder/thunder/core/prims.py

Lines 4082 to 4088 in 52ee541

	def embedding_meta(
	a: TensorProxy, /, weight, *, padding_idx=-1, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False
	) -> TensorProxy:
	# TODO: canonicalize and validating padding idx with weight.shape[0]
	if max_norm is not None:
	raise NotImplementedError

According to the documentation when this argument is active the weight argument is modified inplace. Once we improve general support for inplace operators in Thunder we need to come back to this op and support this variant.