add comments for readability

Author: zewenli98

py/torch_tensorrt/dynamo/conversion/impl/pool.py

@@ -117,19 +117,24 @@ def adaptive_avg_pool1d(
     output_size: Union[int, Sequence[int]],
 ) -> TRTTensor:
     def start_index(idx: int, out_dim: int, in_dim: int) -> int:
+        """Calculate the start index of each pooling window"""
         return math.floor((float(idx) * float(in_dim)) / out_dim)
 
     def end_index(idx: int, out_dim: int, in_dim: int) -> int:
+        """Calculate the end index of each pooling window"""
         return math.ceil((float(idx + 1) * float(in_dim)) / out_dim)
 
     in_dim = input.shape[-1]
     out_dim = output_size if isinstance(output_size, int) else output_size[0]
     output_list = []
 
+    # iterate over each output dimension
     for i in range(out_dim):
+        # calculate the start and end index of each pooling window
         start = start_index(i, out_dim, in_dim)
         end = end_index(i, out_dim, in_dim)
 
+        # slice the input tensor from start to end index, the result of which is the window waiting for pooling
         slices = []
         for j in range(start, end):
             slice = impl.select.select(
@@ -148,6 +153,7 @@ def end_index(idx: int, out_dim: int, in_dim: int) -> int:
         slices = impl.cat.cat(
             ctx, target, source_ir, f"{name}_slices_cat_{i}", slices, dim=-1
         )
+        # calculate the mean of the slices (average pooling output) and append to the output list
         output_list.append(
             impl.reduce.mean(
                 ctx, target, source_ir, f"{name}_sum_{i}", slices, dim=-1, keepdim=True