Consistent naming patterns

Maintain consistent naming conventions throughout the codebase to enhance readability and reduce confusion. This applies to:

1. Import aliases: Use standardized aliases for imported libraries. When working with both MXNet’s NumPy and the official NumPy, use ‘onp’ for the official NumPy to clearly distinguish between them:

   import numpy as onp  # Official NumPy
   from mxnet import np  # MXNet NumPy
   

2. Parameter naming: Use semantically appropriate parameter names that indicate their purpose. Avoid using ‘self’ outside of class methods, and prefer descriptive names like ‘array’ or ‘x’ for data parameters:

   # Good
   def matrix_transpose(x: ndarray, /) -> ndarray:
       # implementation
      
   # Avoid
   def matrix_transpose(self: ndarray, /) -> ndarray:
       # implementation
   

3. Loop variables: Use descriptive names for loop variables that represent what they iterate over:

   # Good
   for key, value in data_shape.items():
       # implementation
      
   # Avoid
   for obj in data_shape:
       # implementation
   

4. Type annotations: Maintain a consistent style for type annotations throughout the codebase, with explicit type names for parameters and return values.

5. Related variables: Use consistent prefixes or suffixes for related variables (e.g., ‘running_mean’ and ‘running_var’ instead of mixing ‘moving_’ and ‘running_’ prefixes).