Enabling Optimizer checkpointing for KeyValueEmbeddingFusedOptimizer

Summary:
**Context:**

1. We Introduced KeyValueEmbeddingFusedOptimizer for SSD Optimizer offloading
https://www.internalfb.com/code/fbsource/[a43d796a4169]/fbcode/torchrec/distributed/batched_embedding_kernel.py?lines=341-376

But, currently the optimizer weights for SSD use-cases are not offloaded and are still on HBM
Refer optimizer state dict CP:
https://www.internalfb.com/code/fbsource/[6303aefbae20]/fbcode/minimal_viable_ai/core/model_family_api/optimizer.py?lines=1019-1028

Due to this, we want to initialize the optimizer class for SSD that allows us to get the latest optimizer weights values during checkpointing (get_optimizer_state call):

https://www.internalfb.com/code/fbsource/[6303aefbae20]/fbcode/deeplearning/fbgemm/fbgemm_gpu/fbgemm_gpu/tbe/ssd/training.py?lines=2540-2545

**Hence, In this Diff:**

We have made the following changes:

1. Loop through every embedding table
a. Change the table placement to CPU
b. Create a ShardedTensor for embedding weight
c. Create a ShardedTensor for optimizer weight
--> There are three cases for optimizers
--> Single Optimizer Value per Shard
--> Row-wise Optimizer value per Shard
--> Point-wise Optimizer value per Shard

and then initialize the optimizer class with the appropriate parameters

Differential Revision: D78131693

Author: Raahul Kalyaan Jakka