[Relax][Frontend][TFLite] Support StableHLO shape ops

[Mohxen]

Summary

Adds Relax TFLite frontend support for the remaining StableHLO shape operators:

• STABLEHLO_RESHAPE -> R.reshape
• STABLEHLO_SLICE -> R.strided_slice
• STABLEHLO_TRANSPOSE -> R.permute_dims

Related to #19519.

Testing

• python -m ruff check python/tvm/relax/frontend/tflite/tflite_frontend.py tests/python/relax/test_frontend_tflite.py
• python -m py_compile python/tvm/relax/frontend/tflite/tflite_frontend.py tests/python/relax/test_frontend_tflite.py
• git diff --check

Related to #19519.

[gemini-code-assist]

Code Review

This pull request adds support for converting the STABLEHLO_RESHAPE operator to Relax in the TFLite frontend, including corresponding unit tests. The feedback suggests replacing the assert statements in the new conversion function with explicit ValueError checks to ensure validation is not bypassed when Python is run with optimization flags.

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[gemini-code-assist]

Using assert statements for input validation is discouraged because they can be optimized away when Python is run with the -O (optimize) flag. This would bypass the validation checks entirely and could lead to cryptic errors (like IndexError) later in the execution. It is safer and more robust to explicitly check the conditions and raise a ValueError with a descriptive error message.

Suggested change

	input_tensors = self.get_input_tensors(op)
	assert len(input_tensors) == 1
	output_tensors = self.get_output_tensors(op)
	assert len(output_tensors) == 1
	input_tensors = self.get_input_tensors(op)
	if len(input_tensors) != 1:
	raise ValueError(f"STABLEHLO_RESHAPE expects exactly 1 input tensor, but got {len(input_tensors)}")
	output_tensors = self.get_output_tensors(op)
	if len(output_tensors) != 1:
	raise ValueError(f"STABLEHLO_RESHAPE expects exactly 1 output tensor, but got {len(output_tensors)}")