[Relax][Frontend][TFLite] Add DENSIFY operator test and fix prefetched handling

[Aharrypotter]

Summary

This PR adds test coverage for the TFLite DENSIFY operator as requested in issue #18971 , and fixes several related bugs in the TFLite frontend.
DENSIFY converts sparse weight tensors to dense format at conversion time (not runtime). The dense weights become constants in the output IR via the prefetched_nodes mechanism.

Changes

Bug Fixes

1. convert_op_to_relax: Check ret is None before normalize(ret) to avoid crash when DENSIFY returns None.
2. get_tensor_expr: Add is_prefetched() check before get_tensor_value() to handle DENSIFY outputs with empty buffers.
3. convert_fully_connected: Add prefetched weight handling
4. convert_transpose_conv: Add prefetched weight handling

Tests

Four test cases covering different DENSIFY usage scenarios:

Test	Downstream Op	Purpose
test_densify	None	Basic DENSIFY to constant
test_densify_with_add	ADD	Prefetched as regular input
test_densify_with_conv2d	CONV2D	Network-level test (2D conv)
test_densify_with_fully_connected	FULLY_CONNECTED	Network-level test (FC layer)

Note: Sparse TFLite models are built manually using flatbuffers API (TensorFlow does not provide an API for creating sparse models).

Testing

All tests pass:

pytest tests/python/relax/test_frontend_tflite.py::test_densify \
       tests/python/relax/test_frontend_tflite.py::test_densify_with_add \
       tests/python/relax/test_frontend_tflite.py::test_densify_with_conv2d \
       tests/python/relax/test_frontend_tflite.py::test_densify_with_fully_connected -v

• test_densify PASSED
• test_densify_with_add PASSED
• test_densify_with_conv2d PASSED
• test_densify_with_fully_connected PASSED

References

Issue #18971 : TFLite operator test coverage tracking
Related: #19408 (MATRIX_DIAG, MATRIX_SET_DIAG, SPARSE_TO_DENSE tests)

[gemini-code-assist]

Code Review

This pull request introduces support for the TFLite DENSIFY operator in the Relax frontend, enabling sparse weight tensors to be converted to dense constants during model conversion. The implementation includes a new get_tensor_value_or_prefetched helper and refactors get_tensor_expr to handle prefetched nodes. Comprehensive tests were added, utilizing manually constructed sparse TFLite models to verify the conversion logic across various operators. Feedback suggests simplifying the convert_fully_connected implementation by using the get_tensor_expr helper method to reduce redundancy.