Error in IREE official pytorch JIT Compilation Notebook

### What happened?

The last cell in the IREE official [JIT Compilation Notebook](https://colab.research.google.com/github/iree-org/iree/blob/main/samples/colab/pytorch_jit.ipynb) fails to execute.

Error:
```module {
func.func @main(%arg0: !torch.vtensor<[4,3],f32>, %arg1: !torch.vtensor<[3],f32>, %arg2: !torch.vtensor<[4],f32>) -> (!torch.vtensor<[3],f32>, !torch.vtensor<[1,4],f32>) {
%int0 = torch.constant.int 0
%0 = torch.aten.unsqueeze %arg2, %int0 : !torch.vtensor<[4],f32>, !torch.int -> !torch.vtensor<[1,4],f32>
%1 = torch.aten.mm %0, %arg0 : !torch.vtensor<[1,4],f32>, !torch.vtensor<[4,3],f32> -> !torch.vtensor<[1,3],f32>
%int0_0 = torch.constant.int 0
%2 = torch.aten.squeeze.dim %1, %int0_0 : !torch.vtensor<[1,3],f32>, !torch.int -> !torch.vtensor<[3],f32>
%int1 = torch.constant.int 1
%3 = torch.aten.add.Tensor %2, %arg1, %int1 : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
return %3, %0 : !torch.vtensor<[3],f32>, !torch.vtensor<[1,4],f32>
}
}

#map = affine_map<(d0) -> (d0)>
module {
util.func public @main$async(%arg0: !hal.buffer_view, %arg1: !hal.buffer_view, %arg2: !hal.buffer_view, %arg3: !hal.fence, %arg4: !hal.fence) -> (!hal.buffer_view, !hal.buffer_view) attributes {inlining_policy = #util.inline.never, iree.abi.model = "coarse-fences", iree.abi.stub} {
%cst = arith.constant 0.000000e+00 : f32
%0 = hal.tensor.import wait(%arg3) => %arg0 : !hal.buffer_view -> tensor<4x3xf32>
%1 = hal.tensor.import wait(%arg3) => %arg1 : !hal.buffer_view -> tensor<3xf32>
%2 = hal.tensor.import wait(%arg3) => %arg2 : !hal.buffer_view -> tensor<4xf32>
%expanded = tensor.expand_shape %2 [[0, 1]] output_shape [1, 4] : tensor<4xf32> into tensor<1x4xf32>
%3 = tensor.empty() : tensor<1x3xf32>
%4 = linalg.fill ins(%cst : f32) outs(%3 : tensor<1x3xf32>) -> tensor<1x3xf32>
%5 = linalg.matmul ins(%expanded, %0 : tensor<1x4xf32>, tensor<4x3xf32>) outs(%4 : tensor<1x3xf32>) -> tensor<1x3xf32>
%collapsed = tensor.collapse_shape %5 [[0, 1]] : tensor<1x3xf32> into tensor<3xf32>
%6 = tensor.empty() : tensor<3xf32>
%7 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel"]} ins(%collapsed, %1 : tensor<3xf32>, tensor<3xf32>) outs(%6 : tensor<3xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%11 = arith.addf %in, %in_0 : f32
linalg.yield %11 : f32
} -> tensor<3xf32>
%8:2 = hal.tensor.barrier join(%7, %expanded : tensor<3xf32>, tensor<1x4xf32>) => %arg4 : !hal.fence
%9 = hal.tensor.export %8#0 : tensor<3xf32> -> !hal.buffer_view
%10 = hal.tensor.export %8#1 : tensor<1x4xf32> -> !hal.buffer_view
util.return %9, %10 : !hal.buffer_view, !hal.buffer_view
}
util.func public @main(%arg0: !hal.buffer_view, %arg1: !hal.buffer_view, %arg2: !hal.buffer_view) -> (!hal.buffer_view, !hal.buffer_view) attributes {iree.abi.stub} {
%c-1_i32 = arith.constant -1 : i32
%c0 = arith.constant 0 : index
%device_0 = hal.devices.get %c0 : !hal.device
%0 = util.null : !hal.fence
%fence = hal.fence.create device(%device_0 : !hal.device) flags("None") : !hal.fence
%1:2 = util.call @main$async(%arg0, %arg1, %arg2, %0, %fence) : (!hal.buffer_view, !hal.buffer_view, !hal.buffer_view, !hal.fence, !hal.fence) -> (!hal.buffer_view, !hal.buffer_view)
%status = hal.fence.await until([%fence]) timeout_millis(%c-1_i32) : i32
util.return %1#0, %1#1 : !hal.buffer_view, !hal.buffer_view
}
}

---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[](https://localhost:8080/#) in ()
1 args = torch.randn(4)
----> 2 turbine_output = opt_linear_module(args)
3
4 print("Weight:", linear_module.weight)
5 print("Bias:", linear_module.bias)

19 frames
[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)
1533
1534 def _call_impl(self, *args, **kwargs):

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1542
1543 try:

[/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py](https://localhost:8080/#) in _fn(*args, **kwargs)
449 prior = set_eval_frame(callback)
450 try:
--> 451 return fn(*args, **kwargs)
452 finally:
453 set_eval_frame(prior)

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)
1533
1534 def _call_impl(self, *args, **kwargs):

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1542
1543 try:

[](https://localhost:8080/#) in forward(self, input)
7 self.bias = torch.nn.Parameter(torch.randn(out_features))
8
----> 9 def forward(self, input):
10 return (input @ self.weight) + self.bias
11

[/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py](https://localhost:8080/#) in _fn(*args, **kwargs)
449 prior = set_eval_frame(callback)
450 try:
--> 451 return fn(*args, **kwargs)
452 finally:
453 set_eval_frame(prior)

[/usr/local/lib/python3.10/dist-packages/torch/_dynamo/external_utils.py](https://localhost:8080/#) in inner(*args, **kwargs)
34 @functools.wraps(fn)
35 def inner(*args, **kwargs):
---> 36 return fn(*args, **kwargs)
37
38 return inner

[/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py](https://localhost:8080/#) in forward(*runtime_args)
915 full_args.extend(params_flat)
916 full_args.extend(runtime_args)
--> 917 return compiled_fn(full_args)
918
919 # Just for convenience

[/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/utils.py](https://localhost:8080/#) in g(args)
87 def make_boxed_func(f):
88 def g(args):
---> 89 return f(*args)
90
91 g._boxed_call = True # type: ignore[attr-defined]

[/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py](https://localhost:8080/#) in runtime_wrapper(*args)
86 args_[idx] = args_[idx].detach()
87 with torch.autograd._force_original_view_tracking(True):
---> 88 all_outs = call_func_at_runtime_with_args(
89 compiled_fn,
90 args_,

[/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/utils.py](https://localhost:8080/#) in call_func_at_runtime_with_args(f, args, steal_args, disable_amp)
111 with context():
112 if hasattr(f, "_boxed_call"):
--> 113 out = normalize_as_list(f(args))
114 else:
115 # TODO: Please remove soon

[/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/utils.py](https://localhost:8080/#) in g(args)
87 def make_boxed_func(f):
88 def g(args):
---> 89 return f(*args)
90
91 g._boxed_call = True # type: ignore[attr-defined]

[/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py](https://localhost:8080/#) in apply(cls, *args, **kwargs)
596 # See NOTE: [functorch vjp and autograd interaction]
597 args = _functorch.utils.unwrap_dead_wrappers(args)
--> 598 return super().apply(*args, **kwargs) # type: ignore[misc]
599
600 if not is_setup_ctx_defined:

[/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py](https://localhost:8080/#) in forward(ctx, *deduped_flat_tensor_args)
503 # of the original view, and not the synthetic base
504
--> 505 fw_outs = call_func_at_runtime_with_args(
506 CompiledFunction.compiled_fw,
507 args,

[/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/utils.py](https://localhost:8080/#) in call_func_at_runtime_with_args(f, args, steal_args, disable_amp)
111 with context():
112 if hasattr(f, "_boxed_call"):
--> 113 out = normalize_as_list(f(args))
114 else:
115 # TODO: Please remove soon

[/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/utils.py](https://localhost:8080/#) in g(args)
87 def make_boxed_func(f):
88 def g(args):
---> 89 return f(*args)
90
91 g._boxed_call = True # type: ignore[attr-defined]

[/usr/local/lib/python3.10/dist-packages/shark_turbine/dynamo/executor.py](https://localhost:8080/#) in __call__(self, *inputs)
91
92 # Move inputs to the device and add to arguments.
---> 93 self._inputs_to_device(inputs, arg_list)
94 # TODO: Append semaphores for async execution.
95

[/usr/local/lib/python3.10/dist-packages/shark_turbine/dynamo/executor.py](https://localhost:8080/#) in _inputs_to_device(self, inputs, arg_list)
106 # Since this is already a fallback case, just use the numpy array interop.
107 # It isn't great, but meh... fallback case.
--> 108 device_array = asdevicearray(self.device_state.device, input_cpu)
109 arg_list.push_ref(device_array._buffer_view)
110

[/usr/local/lib/python3.10/dist-packages/iree/runtime/array_interop.py](https://localhost:8080/#) in asdevicearray(device, a, dtype, implicit_host_transfer, memory_type, allowed_usage, element_type)
265 )
266 # First get an ndarray. Needs to be C-contiguous, enforcing it here.
--> 267 a = np.asarray(a, dtype=dtype, order="C")
268 element_type = map_dtype_to_element_type(a.dtype)
269 if element_type is None:

ValueError: object __array__ method not producing an array```
```

### Steps to reproduce your issue

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2. Click on '....'
3. Scroll down to '....'
4. See error

### What component(s) does this issue relate to?

Compiler, Runtime

### Version information

iree-turbine, Version: 2.3.1
IREE compiler version 20240621.931 @ ac418d1f45d562bf9e9675bf69606c7d718e2432
LLVM version 19.0.0git
Optimized build

PyTorch version: 2.3.0+cpu

### Additional context

_No response_