Fix broken references

Author: ricardoV94

doc/core_development_guide.rst

@@ -26,4 +26,4 @@ some of them might be outdated though:
 
 * :ref:`unittest` -- Tutorial on how to use unittest in testing PyTensor.
 
-* :ref:`sparse` -- Description of the ``sparse`` type in PyTensor.
+* :ref:`libdoc_sparse` -- Description of the ``sparse`` type in PyTensor.

doc/extending/creating_a_c_op.rst

@@ -923,7 +923,7 @@ pre-defined macros.  These section tags have no macros: ``init_code``,
 discussed below.
 
 *      ``APPLY_SPECIFIC(str)`` which will automatically append a name
-       unique to the :ref:`Apply` node that applies the `Op` at the end
+       unique to the :ref:`apply` node that applies the `Op` at the end
        of the provided ``str``. The use of this macro is discussed
        further below.
 
@@ -994,7 +994,7 @@ Apply node in their own names to avoid conflicts between the different
 versions of the apply-specific code. The code that wasn't
 apply-specific was simply defined in the ``c_support_code`` method.
 
-To make indentifiers that include the :ref:`Apply` node name use the
+To make indentifiers that include the :ref:`apply` node name use the
 ``APPLY_SPECIFIC(str)`` macro. In the above example, this macro is
 used when defining the functions ``vector_elemwise_mult`` and
 ``vector_times_vector`` as well as when calling function

doc/extending/creating_an_op.rst

@@ -7,7 +7,7 @@ Creating a new :class:`Op`: Python implementation
 So suppose you have looked through the library documentation and you don't see
 a function that does what you want.
 
-If you can implement something in terms of an existing :ref:`Op`, you should do that.
+If you can implement something in terms of an existing :ref:`op`, you should do that.
 Odds are your function that uses existing PyTensor expressions is short,
 has no bugs, and potentially profits from rewrites that have already been
 implemented.

doc/extending/inplace.rst

@@ -200,7 +200,7 @@ input(s)'s memory). From there, go to the previous section.
    certainly lead to erroneous computations.
 
    You can often identify an incorrect `Op.view_map` or :attr:`Op.destroy_map`
-   by using :ref:`DebugMode`.
+   by using :ref:`DebugMode <debugmode>`.
 
 .. note::
    Consider using :class:`DebugMode` when developing

doc/extending/other_ops.rst

@@ -197,7 +197,7 @@ Want C speed without writing C code for your new Op? You can use Numba
 to generate the C code for you! Here is an `example
 Op <https://gist.github.com/nouiz/5492778#file-theano_op-py>`_ doing that.
 
-.. _alternate_PyTensor_types:
+.. _alternate_pytensor_types:
 
 Alternate PyTensor Types
 ========================

doc/library/tensor/random/index.rst

@@ -83,7 +83,7 @@ Low-level objects
 .. automodule:: pytensor.tensor.random.op
    :members: RandomVariable, default_rng
 
-..automodule:: pytensor.tensor.random.type
+.. automodule:: pytensor.tensor.random.type
    :members: RandomType, RandomGeneratorType, random_generator_type
 
 .. automodule:: pytensor.tensor.random.var

doc/tutorial/examples.rst

@@ -347,15 +347,7 @@ afterwards compile this expression to get functions,
 using pseudo-random numbers is not as straightforward as it is in
 NumPy, though also not too complicated.
 
-The way to think about putting randomness into PyTensor's computations is
-to put random variables in your graph. PyTensor will allocate a NumPy
-`RandomStream` object (a random number generator) for each such
-variable, and draw from it as necessary. We will call this sort of
-sequence of random numbers a *random stream*. *Random streams* are at
-their core shared variables, so the observations on shared variables
-hold here as well. PyTensor's random objects are defined and implemented in
-:ref:`RandomStream<libdoc_tensor_random_utils>` and, at a lower level,
-in :ref:`RandomVariable<libdoc_tensor_random_basic>`.
+The general user-facing API is documented in :ref:`RandomStream<libdoc_tensor_random_basic>`
 
 For a more technical explanation of how PyTensor implements random variables see :ref:`prng`.