Enable Hybrid Descriptor to be compressed (#4297)

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-->

## Summary by CodeRabbit

- **New Features**
- Introduced a new method `enable_compression` in the `DescrptHybrid`
class, allowing users to configure compression settings related to
neighbor distance and table parameters.
- **Documentation**
- Enhanced documentation for the `call` method, providing clearer
descriptions of parameters and return values.

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: cherryWangY

deepmd/dpmodel/descriptor/hybrid.py

@@ -210,6 +210,38 @@ def get_stat_mean_and_stddev(
             stddev_list.append(stddev_item)
         return mean_list, stddev_list
 
+    def enable_compression(
+        self,
+        min_nbor_dist: float,
+        table_extrapolate: float = 5,
+        table_stride_1: float = 0.01,
+        table_stride_2: float = 0.1,
+        check_frequency: int = -1,
+    ) -> None:
+        """Receive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
+
+        Parameters
+        ----------
+        min_nbor_dist
+            The nearest distance between atoms
+        table_extrapolate
+            The scale of model extrapolation
+        table_stride_1
+            The uniform stride of the first table
+        table_stride_2
+            The uniform stride of the second table
+        check_frequency
+            The overflow check frequency
+        """
+        for descrpt in self.descrpt_list:
+            descrpt.enable_compression(
+                min_nbor_dist,
+                table_extrapolate,
+                table_stride_1,
+                table_stride_2,
+                check_frequency,
+            )
+
     def call(
         self,
         coord_ext,

deepmd/pt/model/descriptor/hybrid.py

@@ -224,6 +224,38 @@ def get_stat_mean_and_stddev(
             stddev_list.append(stddev_item)
         return mean_list, stddev_list
 
+    def enable_compression(
+        self,
+        min_nbor_dist: float,
+        table_extrapolate: float = 5,
+        table_stride_1: float = 0.01,
+        table_stride_2: float = 0.1,
+        check_frequency: int = -1,
+    ) -> None:
+        """Receive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
+
+        Parameters
+        ----------
+        min_nbor_dist
+            The nearest distance between atoms
+        table_extrapolate
+            The scale of model extrapolation
+        table_stride_1
+            The uniform stride of the first table
+        table_stride_2
+            The uniform stride of the second table
+        check_frequency
+            The overflow check frequency
+        """
+        for descrpt in self.descrpt_list:
+            descrpt.enable_compression(
+                min_nbor_dist,
+                table_extrapolate,
+                table_stride_1,
+                table_stride_2,
+                check_frequency,
+            )
+
     def forward(
         self,
         coord_ext: torch.Tensor,