Initial goal was to add SHAPE and RANGE tests, solving part of #18971
This PR achieves that and includes the minimum necessary frontend fixes
discovered during implementation so those tests reflect real supported
behavior instead of xfail/workarounds.
so this PR includes both:
**1. New SHAPE/RANGE tests
2. Targeted frontend fixes required to make those tests pass correctly**
## Why These Changes Were Needed
- SHAPE conversion previously produced symbolic shape info instead of a
tensor output aligned with TFLite SHAPE semantics.
- RANGE conversion passed tensor expressions into arange instead of
scalar values for constant scalar bounds.
- Zero-input TFLite subgraphs (valid for constant-only models such as
RANGE without inputs) were blocked by a strict assertion.
- Model output collection was brittle for constant/prefetched outputs
and could fail when output expressions were not already in the expr
table.
- As a result, i could not add meaningful SHAPE/RANGE coverage without
fixing frontend behavior.
## **Modifications**
### **Frontend Changes** (In tflite_frontend.py):
- Updated convert_shape: SHAPE now materializes shape output as a tensor
using shape_to_tensor(shape_of(...))
- Applies output dtype casting based on ShapeOptions OutType
(int32/int64)
- Updated convert_range: Extracts scalar values for start/limit/delta
from scalar constants
- Calls arange with scalar-like values
- Keeps dynamic scalar RANGE explicit as unsupported (raises
OpNotImplemented with clear message)
- Updated _input_type: Removed assumption that every subgraph must have
at least one input
- Supports valid zero-input subgraphs
- Updated from_tflite output assembly: Resolves outputs via tensor
wrappers and get_tensor_expr instead of direct expr-table lookup by name
---
**Main functional changes are localized to SHAPE/RANGE conversion and
model output/input handling.**
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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Bana
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