Add dims/coords consistency validation before sampling (issue #7891)

[atheendre130505]

This commit implements validation of InferenceData dimensions and coordinates consistency before pm.sample() executes, preventing cryptic shape mismatch errors during sampling.

Changes:

• Add pymc/model/validation.py with validation functions:

  • validate_dims_coords_consistency(): Main validation entry point
  • check_dims_exist(): Verify referenced dims exist in model.coords
  • check_shape_dims_match(): Verify variable shapes match declared dims
  • check_coord_lengths(): Verify coordinate lengths match dimension sizes

• Integrate validation into pymc/sampling/mcmc.py:

  • Added validation call early in sample() function, before sampling setup
  • Provides clear, actionable error messages to guide users

• Add comprehensive tests in tests/model/test_dims_coords_validation.py:

  • Test missing coord detection
  • Test shape-dims mismatch detection
  • Test coordinate length validation
  • Test MutableData, observed data, and Deterministic variables
  • Test edge cases and complex models

Fixes #7891

Description

Related Issue

• Closes #
• Related to #

Checklist

• Checked that the pre-commit linting/style checks pass
• Included tests that prove the fix is effective or that the new feature works
• Added necessary documentation (docstrings and/or example notebooks)
• If you are a pro: each commit corresponds to a relevant logical change

Type of change

• New feature / enhancement
• Bug fix
• Documentation
• Maintenance
• Other (please specify):

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📚 Documentation preview 📚: https://pymc--7943.org.readthedocs.build/en/7943/