[Bug] a typo mistake in pytorch frontend nonzero_numpy

[taomiao]

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Expected behavior

work well

Actual behavior

test_nonzero_numpy.py:45: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../../../python/tvm/relay/frontend/pytorch.py:5418: in from_pytorch
    outputs = converter.convert_operators(operator_nodes, outputs, ret_name)
../../../../python/tvm/relay/frontend/pytorch.py:4528: in convert_operators
    unpacked = _unpack_tuple(inputs[0])
../../../../python/tvm/relay/frontend/pytorch.py:5137: in _unpack_tuple
    elif isinstance(tup.type_annotation, TupleType):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = CallNode(Op(argwhere), [Var(input1, ty=TensorType([2, 10], bool))], (nullptr), [])
name = 'type_annotation'

    def __getattr__(self, name):
        # specially check handle since
        # this is required for PackedFunc calls
        if name == "handle":
            raise AttributeError("handle is not set")
    
        try:
            return _ffi_node_api.NodeGetAttr(self, name)
        except AttributeError:
>           raise AttributeError(f"{type(self)} has no attribute {name}") from None
E           AttributeError: <class 'tvm.relay.expr.Call'> has no attribute type_annotation

Environment

os: ubuntu
python: 3.9
pytorch: 2.0
tvm: main branch

Steps to reproduce

from torch import nn
import torch
import tvm


class NonZeroModule(nn.Module):
    """Module that performs nonzero"""

    def __init__(self):
        super().__init__()

    def forward(self, x, mask):
        mask_index = torch.nonzero(mask, as_tuple=True)
        x[mask_index] = torch.ones_like(x[mask_index])
        return x

def test_pytorch_nonzero():
    model = NonZeroModule()
    x = torch.zeros((2, 10), dtype=torch.float32)
    mask = torch.randint(0, 2, (2, 10)).bool()
    with torch.no_grad():
        traced_torch_model = torch.jit.trace(model, (x, mask))
    import_input = [("input0", (2, 10)), ("input1", (2, 10))]
    relay_model_ir, relay_model_params = tvm.relay.frontend.from_pytorch(
        traced_torch_model, import_input
    )

Triage

Please refer to the list of label tags here to find the relevant tags and add them below in a bullet format (example below).

• needs-triage
• frontend:pytorch