docs: add the docstrings for v1.0.0 (#656)

* add v2 flux examples

* add the docs

* add docs

* update

* finished ops

* add ops

* update

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* update docstrings

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* finished the api docs

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nunchaku/ops/quantize.py

 """
-Python wrappers for Nunchaku's quantization operations.
+This module provides Python wrappers for Nunchaku's high-performance SVDQuant quantization CUDA kernels.
 """
 
 import torch
@@ -18,22 +18,49 @@ def svdq_quantize_w4a4_act_fuse_lora_cuda(
     fuse_glu: bool = False,
     fp4: bool = False,
     pad_size: int = 256,
-) -> torch.Tensor:
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     """
-    This function wraps the high-performance CUDA kernel for SVDQuant W4A4 quantized GEMM.
-
-    Notation
-    --------
-    M : int
-        Batch size (number of input samples).
-    K : int
-        Number of input channels (feature dimension).
-    N : int
-        Number of output channels.
-    G : int
-        Number of groups. 64 for INT4 and 16 for NVFP4.
-    R : int
-        Rank of the low-rank branch.
+    Quantizes activations and computes LoRA down-projection using SVDQuant W4A4 CUDA kernel.
+
+    Parameters
+    ----------
+    input : torch.Tensor, shape (M, K), dtype bfloat16/float16
+        Input activations.
+    output : torch.Tensor or None, shape (M_pad, K // 2), dtype uint8, optional
+        Packed output tensor for quantized activations. Allocated if None.
+    oscales : torch.Tensor or None, shape (K // G, M_pad), dtype float8_e4m3fn for NVFP4 or input dtype for INT4, optional
+        Output scales tensor. Allocated if None.
+    lora_down : torch.Tensor or None, shape (K, R), dtype bfloat16/float16, optional
+        Packed LoRA down-projection weights.
+    lora_act_out : torch.Tensor or None, shape (M_pad, R), dtype float32, optional
+        Packed output tensor for LoRA activations. Allocated if None.
+    smooth : torch.Tensor or None, optional, dtype bfloat16/float16
+        Smoothing factor for quantization.
+    fuse_glu : bool, default=False
+        If True, fuse GLU activation.
+    fp4 : bool, default=False
+        If True, use NVFP4 quantization; else INT4.
+    pad_size : int, default=256
+        Pad batch size to a multiple of this value for efficient CUDA execution.
+
+    Returns
+    -------
+    output : torch.Tensor, shape (M_pad, K // 2), dtype uint8
+        Packed quantized activations.
+    oscales : torch.Tensor, shape (K // G, M_pad), dtype float8_e4m3fn for NVFP4 or input dtype for INT4
+        Output scales.
+    lora_act_out : torch.Tensor, shape (M_pad, R), dtype float32
+        Packed LoRA activation output.
+
+    Notes
+    -----
+    Notations:
+
+    - M: batch size
+    - K: input channels
+    - R: LoRA rank
+    - G: group size (64 for INT4, 16 for NVFP4)
+    - M_pad: padded batch size = ceil(M / pad_size) * pad_size
     """
     batch_size, channels = input.shape
     rank = lora_down.shape[1]