[Relax][ONNX] Complete ShapeExpr reshape handling in ONNX frontend

[LudovicoYIN]

Summary

Complete Reshape handling for shape values in the Relax ONNX frontend.

Changes

• keep ShapeExpr -> Reshape([-1]) on the shape-specialized path
• materialize ShapeExpr to an int64 tensor for other reshape targets and apply regular tensor reshape semantics
• add frontend coverage for Shape -> Reshape([-1])
• add frontend coverage for reshaping shape outputs to non-[-1] targets such as [1, 3] and [3, 1]
• extend symbolic shape deduction coverage to include the common Shape -> Reshape([-1]) -> Gather -> Unsqueeze shape-construction pattern

Validation

• pytest -k 'test_symbolic_shape_deduction or test_reshape_shape_output or test_reshape'

This PR completes the Reshape limitation in the Relax ONNX frontend operator work tracked in #18945.

[gemini-code-assist]

Code Review

This pull request updates the ONNX frontend to improve the handling of Reshape operations when applied to relax.ShapeExpr inputs. It ensures that identity flattens are preserved to maintain specialized shape handling, while other reshape targets are correctly converted to tensor operations. Corresponding tests were added and updated to verify these changes, including symbolic shape deduction. Feedback was provided to refactor the test node construction for better readability and conciseness.

[gemini-code-assist]

For improved readability and conciseness, the construction of the nodes list can be refactored. Creating the nodes directly within nodes.append and nodes.extend calls avoids unnecessary intermediate variables, making the graph's structure more direct and easier to understand.

Suggested change

	shape_node = helper.make_node("Shape", ["data"], ["shape_output"])
	gather_node = helper.make_node("Gather", ["shape_output", "indices"], ["gather_output"])
	nodes = [index_node, shape_node]
	gather_input = "shape_output"
	if with_reshape_flatten:
	reshape_node = helper.make_node(
	"Reshape", ["shape_output", "target_shape"], ["reshaped_shape"]
	)
	nodes.append(reshape_node)
	gather_input = "reshaped_shape"
	gather_node = helper.make_node("Gather", [gather_input, "indices"], ["gather_output"])
	unsqueeze_node = helper.make_node("Unsqueeze", ["gather_output", "axes"], ["unsqueeze_output"])
	constant_of_shape_node = helper.make_node(
	"ConstantOfShape",
	["unsqueeze_output"],
	["output"],
	value=helper.make_tensor("value", TensorProto.FLOAT, [], [1]),
	)
	nodes.extend([gather_node, unsqueeze_node, constant_of_shape_node])
	shape_node = helper.make_node("Shape", ["data"], ["shape_output"])
	nodes = [index_node, shape_node]
	gather_input = "shape_output"
	if with_reshape_flatten:
	nodes.append(
	helper.make_node("Reshape", ["shape_output", "target_shape"], ["reshaped_shape"])
	)
	gather_input = "reshaped_shape"
	nodes.extend([
	helper.make_node("Gather", [gather_input, "indices"], ["gather_output"]),
	helper.make_node("Unsqueeze", ["gather_output", "axes"], ["unsqueeze_output"]),
	helper.make_node(
	"ConstantOfShape",
	["unsqueeze_output"],
	["output"],
	value=helper.make_tensor("value", TensorProto.FLOAT, [], [1]),
	),
	])

[tlopex]

LGTM! Thank you!