[Relax][PyTorch] Cast non-bool inputs to bool in logical_and converter

[javierdejesusda]

Motivation

torch.logical_and accepts input tensors of any dtype (treating any nonzero
element as True) and always returns a bool tensor.

The PyTorch frontend did not produce that bool result. The ExportedProgram
frontend lowered logical_and.default with
self._binary_op(relax.op.logical_and, operator.and_), which kept the operand
dtype and emitted relax.op.logical_and on non-bool inputs (for example
float32). relax.op.logical_and requires boolean inputs and otherwise fails
LegalizeOps in the TOPI logical_and. The FX frontend did not register
logical_and at all, so the op was unsupported there.

Changes

• Add a shared _logical_and converter in BaseFXGraphImporter that casts
  non-bool operands to bool before applying relax.op.logical_and. Bool
  operands are passed through unchanged (no redundant cast).
• Point the logical_and.default (ExportedProgram) registration at the new
  converter, and add a logical_and (FX) registration that was previously
  missing, matching the existing logical_not converter.
• Add a standalone test_logical_and to both the FX and ExportedProgram test
  suites asserting the corrected IR (astype to bool on each operand, then
  logical_and, producing a bool output).

Notes

The cast to bool lowers to an elementwise nonzero test, so it matches
PyTorch's "nonzero is True" semantics for float, integer, and NaN inputs.

[gemini-code-assist]

Code Review

This pull request implements a custom translator for torch.logical_and in the TVM Relax PyTorch frontend. The new _logical_and method casts non-boolean inputs to boolean first, aligning with PyTorch's behavior. This translator is registered in both FX and Exported Program translators, and corresponding tests are added. A review comment suggests adding safety checks to ensure struct_info is an instance of relax.TensorStructInfo before accessing its dtype to prevent potential AttributeErrors.

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[cchung100m]

LGTM, thanks to @javierdejesusda