Fixed links

Author: Tim Joseph

docs/tensor_distribution/development.md

@@ -1,58 +1,58 @@
 # TensorDistribution Development Guide
 
-This document outlines the design requirements and implementation patterns for [`TensorDistribution`](src/tensorcontainer/tensor_distribution/base.py:14) and its subclasses in the [`src/tensorcontainer/tensor_distribution/`](src/tensorcontainer/tensor_distribution/) module.
+This document outlines the design requirements and implementation patterns for [`TensorDistribution`](/src/tensorcontainer/tensor_distribution/base.py) and its subclasses in the [`src/tensorcontainer/tensor_distribution/`](/src/tensorcontainer/tensor_distribution/) module.
 
 ## Objective
 
-The [`tensor_distribution`](src/tensorcontainer/tensor_distribution/) module provides [`torch.distributions.Distribution`](https://pytorch.org/docs/stable/distributions.html#torch.distributions.distribution.Distribution) functionality that enables direct application of tensor operations to probability distributions. The module maintains complete signature compatibility with [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) while extending [`TensorContainer`](src/tensorcontainer/tensor_container.py:1) functionality through [`TensorAnnotated`](src/tensorcontainer/tensor_annotated.py) inheritance.
+The [`tensor_distribution`](/src/tensorcontainer/tensor_distribution/) module provides `torch.distributions.Distribution` functionality that enables direct application of tensor operations to probability distributions. The module maintains complete signature compatibility with `torch.distributions` while extending [`TensorContainer`](/src/tensorcontainer/tensor_container.py) functionality through [`TensorAnnotated`](/src/tensorcontainer/tensor_annotated.py) inheritance.
 
 ## Architecture Requirements
 
 ### Signature Compatibility
 
-All classes in [`tensorcontainer.tensor_distribution`](src/tensorcontainer/tensor_distribution/) must maintain exact signature compatibility with their corresponding [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) classes. This compatibility is enforced through automated testing using [`tests/tensor_distribution/conftest.py::assert_init_signatures_match`](tests/tensor_distribution/conftest.py).
+All classes in [`tensorcontainer.tensor_distribution`](/src/tensorcontainer/tensor_distribution/) must maintain exact signature compatibility with their corresponding `torch.distributions` classes. This compatibility is enforced through automated testing using [`tests/tensor_distribution/conftest.py::assert_init_signatures_match`](/tests/tensor_distribution/conftest.py).
 
-**Implementation Requirement**: When [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) classes lack proper type annotations for [`__init__`](src/tensorcontainer/tensor_distribution/base.py:65) parameters, implementers **must** consult the class docstring to determine correct type hints.
+**Implementation Requirement**: When `torch.distributions` classes lack proper type annotations for `__init__` parameters, implementers **must** consult the class docstring to determine correct type hints.
 
 ### Distribution Delegation Pattern
 
-[`TensorDistribution`](src/tensorcontainer/tensor_distribution/base.py:14) subclasses **must not** implement distribution-specific logic. Instead, each subclass **must** implement a [`dist()`](src/tensorcontainer/tensor_distribution/base.py:143) method that constructs and returns the equivalent [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) object using the instance's parameters.
+[`TensorDistribution`](/src/tensorcontainer/tensor_distribution/base.py) subclasses **must not** implement distribution-specific logic. Instead, each subclass **must** implement a `dist()` method that constructs and returns the equivalent `torch.distributions` object using the instance's parameters.
 
-**Implementation Requirement**: The [`dist()`](src/tensorcontainer/tensor_distribution/base.py:143) method **must** return the raw [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) instance, not a wrapped one (e.g., with [`Independent`](https://pytorch.org/docs/stable/distributions.html#torch.distributions.independent.Independent)).
+**Implementation Requirement**: The `dist()` method **must** return the raw `torch.distributions` instance, not a wrapped one (e.g., with `Independent`).
 
-**Design Principle**: [`TensorDistribution`](src/tensorcontainer/tensor_distribution/base.py:14) serves as a parameter management wrapper around [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html), delegating all distribution operations to the underlying implementation via [`self.dist()`](src/tensorcontainer/tensor_distribution/base.py:143) calls.
+**Design Principle**: [`TensorDistribution`](/src/tensorcontainer/tensor_distribution/base.py) serves as a parameter management wrapper around `torch.distributions`, delegating all distribution operations to the underlying implementation via `self.dist()` calls.
 
 ### Parameter Broadcasting Requirements
 
-Many [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) constructors accept parameters of type `Union[Number, Tensor]` or any specialization of `Number` (e.g. `float`). However, [`TensorContainer`](src/tensorcontainer/tensor_container.py) and [`TensorDistribution`](src/tensorcontainer/tensor_distribution/base.py:14) can only process `Union[Tensor, TensorContainer]` objects and require all parameters to have compatible shapes for broadcasting.
+Many `torch.distributions` constructors accept parameters of type `Union[Number, Tensor]` or any specialization of `Number` (e.g. `float`). However, [`TensorContainer`](/src/tensorcontainer/tensor_container.py) and [`TensorDistribution`](/src/tensorcontainer/tensor_distribution/base.py) can only process `Union[Tensor, TensorContainer]` objects and require all parameters to have compatible shapes for broadcasting.
 
-**Implementation Rule**: When the constructor signature contains `Union[Number, Tensor]` or any specialization of `Number` parameters, implementations **must** use [`torch.distributions.utils.broadcast_all`](https://pytorch.org/docs/stable/distributions.html#torch.distributions.utils.broadcast_all) to:
+**Implementation Rule**: When the constructor signature contains `Union[Number, Tensor]` or any specialization of `Number` parameters, implementations **must** use `torch.distributions.utils.broadcast_all` to:
 1. Convert scalar numbers to tensors
 2. Broadcast all parameters to a common shape
 
-This preprocessing ensures proper shape and device management within the [`TensorAnnotated`](src/tensorcontainer/tensor_annotated.py) framework.
+This preprocessing ensures proper shape and device management within the [`TensorAnnotated`](/src/tensorcontainer/tensor_annotated.py) framework.
 
 **Decision Criterion**: If the constructor signature does not contain `Union[Number, Tensor]` parameters, simpler parameter handling approaches should be preferred.
 
 ### Validation Strategy
 
-[`TensorDistribution`](src/tensorcontainer/tensor_distribution/base.py:14) accepts a [`validate_args`](src/tensorcontainer/tensor_distribution/base.py:69) parameter during initialization and stores it as the [`_validate_args`](src/tensorcontainer/tensor_distribution/base.py:63) attribute of the base class. Subclasses must pass this value to the underlying [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) object (if the constructor supports it).
+[`TensorDistribution`](/src/tensorcontainer/tensor_distribution/base.py) accepts a `validate_args` parameter during initialization and stores it as the `_validate_args` attribute of the base class. Subclasses must pass this value to the underlying `torch.distributions` object (if the constructor supports it).
 
-**Validation Policy**: Parameter validation for [`TensorDistribution`](src/tensorcontainer/tensor_distribution/base.py:14) subclasses is generally unnecessary because the [`TensorDistribution.__init__`](src/tensorcontainer/tensor_distribution/base.py:65) method constructs the underlying distribution once via [`self.dist()`](src/tensorcontainer/tensor_distribution/base.py:86), triggering parameter validation in the [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) implementation.
+**Validation Policy**: Parameter validation for [`TensorDistribution`](/src/tensorcontainer/tensor_distribution/base.py) subclasses is generally unnecessary because the `TensorDistribution.__init__` method constructs the underlying distribution once via `self.dist()`, triggering parameter validation in the `torch.distributions` implementation.
 
 **Exception Handling**: Implementations should only raise validation errors when required parameters needed for device and shape inference are missing or invalid.
 
 ### Property Implementation Pattern
 
-Following the [`torch.distributions.Distribution`](https://pytorch.org/docs/stable/distributions.html#torch.distributions.distribution.Distribution) pattern, basic distribution properties are provided through the [`TensorDistribution`](src/tensorcontainer/tensor_distribution/base.py:14) base class via delegation to [`self.dist()`](src/tensorcontainer/tensor_distribution/base.py:143).
+Following the `torch.distributions.Distribution` pattern, basic distribution properties are provided through the [`TensorDistribution`](/src/tensorcontainer/tensor_distribution/base.py) base class via delegation to `self.dist()`.
 
-**Specialization Rule**: Distribution-specific properties **must** be implemented only in the corresponding subclass, maintaining the same delegation pattern to the underlying [`torch.distributions`](https://pytorch.org/docs/stable/distributions.html) object.
+**Specialization Rule**: Distribution-specific properties **must** be implemented only in the corresponding subclass, maintaining the same delegation pattern to the underlying `torch.distributions` object.
 
 ## Implementation Patterns
 
 ### Annotated Attribute Pattern
 
-All tensor parameters must be declared as annotated class attributes to enable automatic transformation by [`TensorAnnotated`](src/tensorcontainer/tensor_annotated.py) operations (e.g., [`.to()`](src/tensorcontainer/tensor_annotated.py), [`.expand()`](src/tensorcontainer/tensor_annotated.py)).
+All tensor parameters must be declared as annotated class attributes to enable automatic transformation by [`TensorAnnotated`](/src/tensorcontainer/tensor_annotated.py) operations (e.g., `.to()`, `.expand()`).
 
 **Example Pattern**:
 ```python
@@ -73,13 +73,13 @@ Note: If parameters like `loc` and `scale` could be scalars in the constructor s
 
 ### Lazy Distribution Creation
 
-The actual [`torch.distributions.Distribution`](https://pytorch.org/docs/stable/distributions.html#torch.distributions.distribution.Distribution) instance is created on-demand through the [`dist()`](src/tensorcontainer/tensor_distribution/base.py:143) method. This lazy evaluation pattern enables efficient tensor operations without premature distribution instantiation.
+The actual `torch.distributions.Distribution` instance is created on-demand through the `dist()` method. This lazy evaluation pattern enables efficient tensor operations without premature distribution instantiation.
 
 ### Reconstruction Pattern
 
-The [`_unflatten_distribution()`](src/tensorcontainer/tensor_distribution/base.py:115) class method reconstructs distribution instances from serialized tensor and metadata attributes. This method is called by [`_init_from_reconstructed()`](src/tensorcontainer/tensor_distribution/base.py:90) during operations like [`.to()`](src/tensorcontainer/tensor_annotated.py) and [`.expand()`](src/tensorcontainer/tensor_annotated.py).
+The `_unflatten_distribution()` class method reconstructs distribution instances from serialized tensor and metadata attributes. This method is called by `_init_from_reconstructed()` during operations like `.to()` and `.expand()`.
 
-**Customization Requirement**: Subclasses with complex parameter relationships **must** override [`_unflatten_distribution()`](src/tensorcontainer/tensor_distribution/base.py:115) to implement appropriate reconstruction logic.
+**Customization Requirement**: Subclasses with complex parameter relationships **must** override `_unflatten_distribution()` to implement appropriate reconstruction logic.
 
 **Example Implementation**:
 ```python
@@ -91,4 +91,3 @@ def _unflatten_distribution(cls, attributes: Dict[str, Any]):
         logits=attributes.get("_logits"),
         validate_args=attributes.get("_validate_args"),
     )
-```