Add the FTRL (Follow The Regularized Leader) optimizer.

This implementation is based on the FTRL algorithm, [McMahan et al., 2013](https://research.google.com/pubs/archive/41159.pdf).

Features / Params in `FTRLOptimizerSpec` (as used in the primitive):
- **learning_rate**: The base learning rate.
- **learning_rate_power**: Controls the per-coordinate learning rate decay (typically -0.5).
- **l1_regularization_strength**: Applies L1 regularization, which can lead to sparsity in the model weights.
- **l2_regularization_strength**: Applies L2 regularization.
- **beta**: An additional smoothing term..
- **clip_weight_min**, **clip_weight_max**: Optional bounds for clipping the updated embedding weights.
- **weight_decay_factor**: Factor for applying weight decay to the gradients.
- **multiply_weight_decay_factor_by_learning_rate**: Boolean flag; if true, the `weight_decay_factor` is multiplied by the `learning_rate` before applying decay.
- **multiply_linear_by_learning_rate**: Boolean flag; if true, the linear term update incorporates the `learning_rate` differently.
- **allow_zero_accumulator**: Boolean flag; if true, allows the accumulator to be exactly zero. Otherwise, a small epsilon is added for numerical stability when `accumulator` is zero.

The optimizer maintains two slot variables for each trainable embedding parameter:
- **accumulator**: Stores the sum of squared gradients, used to adapt the learning rate on a per-coordinate basis.
- **linear**: Stores a linear combination related to the gradients, which is central to the FTRL weight update rule.

PiperOrigin-RevId: 764794731

Author: Google-ML-Automation

jax_tpu_embedding/sparsecore/lib/core/primitives/BUILD

@@ -135,6 +135,21 @@ pytype_strict_library(
     ],
 )
 
+pytype_strict_library(
+    name = "sparse_dense_matmul_grad_with_ftrl",
+    srcs = [
+        "sparse_dense_matmul_grad_with_ftrl.py",
+    ],
+    deps = [
+        ":utils",
+        "//jax_tpu_embedding/sparsecore/lib/core:constants",
+        pypi_requirement("jax"),
+        pypi_requirement("jax/_src/lib"),
+        pypi_requirement("jax/extend"),
+        pypi_requirement("numpy"),
+    ],
+)
+
 pytype_strict_library(
     name = "optimizers_computation",
     srcs = [
@@ -170,6 +185,7 @@ pytype_strict_library(
         ":optimizers_computation",  # buildcleaner: keep
         ":sparse_dense_matmul_csr",  # buildcleaner: keep
         ":sparse_dense_matmul_grad_with_adagrad",  # buildcleaner: keep
+        ":sparse_dense_matmul_grad_with_ftrl",  # buildcleaner: keep
         ":sparse_dense_matmul_grad_with_laprop",  # buildcleaner: keep
         ":sparse_dense_matmul_grad_with_sgd",  # buildcleaner: keep
         ":sparse_dense_matmul_optimizer_grad",  # buildcleaner: keep