Sort some APIs docs by names (a-z) (#472)

docs/src/python/nn/functions.rst

@@ -15,9 +15,9 @@ simple functions.
    gelu
    gelu_approx
    gelu_fast_approx
-   relu
+   mish
    prelu
+   relu
+   selu
    silu
    step
-   selu
-   mish

docs/src/python/nn/layers.rst

@@ -9,29 +9,29 @@ Layers
    :toctree: _autosummary
    :template: nn-module-template.rst
 
-   Sequential
+   ALiBi
-   ReLU
+   BatchNorm
-   PReLU
-   GELU
-   SiLU
-   Step
-   SELU
-   Mish
-   Embedding
-   Linear
-   QuantizedLinear
    Conv1d
    Conv2d
-   BatchNorm
-   LayerNorm
-   RMSNorm
-   GroupNorm
-   InstanceNorm
    Dropout
    Dropout2d
    Dropout3d
-   Transformer
+   Embedding
+   GELU
+   GroupNorm
+   InstanceNorm
+   LayerNorm
+   Linear
+   Mish
    MultiHeadAttention
-   ALiBi
+   PReLU
+   QuantizedLinear
+   RMSNorm
+   ReLU
    RoPE
+   SELU
+   Sequential
+   SiLU
    SinusoidalPositionalEncoding
+   Step
+   Transformer

docs/src/python/nn/losses.rst

@@ -10,14 +10,14 @@ Loss Functions
    :template: nn-module-template.rst
 
    binary_cross_entropy
+   cosine_similarity_loss
    cross_entropy
+   hinge_loss
+   huber_loss
    kl_div_loss
    l1_loss
+   log_cosh_loss
    mse_loss
    nll_loss
    smooth_l1_loss
-   triplet_loss
+   triplet_loss
-   hinge_loss
-   huber_loss
-   log_cosh_loss
-   cosine_similarity_loss

docs/src/python/random.rst

@@ -33,13 +33,13 @@ we use a splittable version of Threefry, which is a counter-based PRNG.
 .. autosummary:: 
   :toctree: _autosummary
 
-   seed
-   key
-   split
    bernoulli
    categorical
    gumbel
+   key
    normal
    randint
-   uniform
+   seed
+   split
    truncated_normal
+   uniform