fix some docs

Author: refraction-ray

docs/source/api/interfaces.rst

@@ -1,6 +1,7 @@
 tensorcircuit.interfaces
 ================================================================================
 .. toctree::
+    interfaces/jax.rst
     interfaces/numpy.rst
     interfaces/scipy.rst
     interfaces/tensorflow.rst

docs/source/api/interfaces/jax.rst

@@ -0,0 +1,7 @@
+tensorcircuit.interfaces.jax
+================================================================================
+.. automodule:: tensorcircuit.interfaces.jax
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

docs/source/contribution.rst

@@ -250,7 +250,9 @@ And from GitHub page choose draft a release from tag.
     export VERSION=0.x.y
     twine upload dist/tensorcircuit-${VERSION}-py3-none-any.whl dist/tensorcircuit-${VERSION}.tar.gz
 
-For upload authetication via token, please refer `this tutorial <https://kynan.github.io/blog/2020/05/23/how-to-upload-your-package-to-the-python-package-index-pypi-test-server>`__ . (username is ``__token__``)
+For upload authetication via token, please refer `this tutorial <https://kynan.github.io/blog/2020/05/23/how-to-upload-your-package-to-the-python-package-index-pypi-test-server>`__ .
+Latest version of twine direct accepts token.
+
 
 **DockerHub Release**
 

docs/source/sharpbits.rst

@@ -228,16 +228,16 @@ Also see the code below for a reference:
     # [0.90929747 0.90929747]
 
 
-    VMAP outside grad-like function on tensorflow backend
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+VMAP outside grad-like function on tensorflow backend
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-    Vmap (vectorized map) outside a grad-like function may cause incorrected results on TensorFlow backends due to a long existing `bug <https://github.com/tensorflow/tensorflow/issues/52148>`_ in TensorFlow codebase. So better always stick to the first-vmap-then-differentiated paradigm.
+Vmap (vectorized map) outside a grad-like function may cause incorrected results on TensorFlow backends due to a long existing `bug <https://github.com/tensorflow/tensorflow/issues/52148>`_ in TensorFlow codebase. So better always stick to the first-vmap-then-differentiated paradigm.
 
-    Grad over vmap function
-    ~~~~~~~~~~~~~~~~~~~~~~~~~
+Grad over vmap function
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
-    A related issue is the different behavior for `K.grad(K.vmap(f))` on different backends. For tensorflow backend, the function to be differentiated has a scalar output which is the sum of all outputs.
+A related issue is the different behavior for `K.grad(K.vmap(f))` on different backends. For tensorflow backend, the function to be differentiated has a scalar output which is the sum of all outputs.
 
-    However, for Jax backend, the function simply raise error as only scalar output function can be differentiated, no implicit sum of the vectorized ``f`` is assumed. For non-scalar output, one should use `jacrev` or `jacfwd` to get the gradient information.
+However, for Jax backend, the function simply raise error as only scalar output function can be differentiated, no implicit sum of the vectorized ``f`` is assumed. For non-scalar output, one should use `jacrev` or `jacfwd` to get the gradient information.
 
-    Specifically, `K.grad(K.vmap(f))` on TensorFlow backend is equilvalent to `K.grad(K.append(K.vamp(f), K.sum))` on Jax backend.
+Specifically, `K.grad(K.vmap(f))` on TensorFlow backend is equilvalent to `K.grad(K.append(K.vamp(f), K.sum))` on Jax backend.