Fill library node

Filling an entire data container (like an array) with a single value is a common use case in DaCe codes. Instead of manually specifying _how_ a container should be filled, we would like to have a [Library Node](https://spcldace.readthedocs.io/en/latest/sdfg/ir.html#library-nodes) that specifies that an array should be filled with a certain value. This is also a performant feature because it can be specialized. Examples:
* Filling an array with a zero value (or any integer) can be lowered to a `memset` call, or `{cuda,hip}Memset` for GPUs
* Tensor/Matrix Core memory on GPUs can use `wmma::fill_fragment`

The library node should be implemented with one output leading to the data container to be filled, and an optional input connector called `value` which can specify a runtime value to fill. In case the value is constant or symbolic, the input may exist as a property. This is similar to how `alpha`/`beta` values are treated in matrix multiplication library nodes.

The library node will live in the DaCe [Standard](https://github.com/spcl/dace/tree/master/dace/libraries/standard) library, similarly to other basic operations such as Reduce. To help with the implementation, refer to the [einsum](https://github.com/spcl/dace/blob/master/dace/libraries/blas/nodes/einsum.py) library node. To see how input/output connectors can be defined, refer to [TensorTranspose](https://github.com/spcl/dace/blob/master/dace/libraries/standard/nodes/ttranspose.py).

The implementation tasks are as follows:
1. Implement the `Fill` library node in the Standard library
2. Implement a native expansion that uses only SDFG components under the `'pure'` expansion
3. (optional) Implement fast specializations
4. Create Fill library nodes in the Python frontend instead of the manual implementation (replacing the implementation of [numpy.full](https://github.com/spcl/dace/blob/ee5a6dfe695f329c3882105b087f3563a0c80b81/dace/frontend/python/replacements.py#L304))