[Bug] tensordot produce inconsistency inference results when execuing twice under the same inputs

`topi.tensordot` cannot output a determined results under the same inputs. It's weird.

### Actual behavior

```
AssertionError:
Not equal to tolerance rtol=0.001, atol=0.001
An Inconsistency bug detected.
Mismatched elements: 1 / 1 (100%)
Max absolute difference among violations: 3.686663e+32
Max relative difference among violations: 1.
ACTUAL: array([-1.109184], dtype=float32)
DESIRED: array([3.686663e+32], dtype=float32)
```

### Environment

tvm-0.21.dev0

### Steps to reproduce

```
import tvm
from tvm import te, topi, tir
from tvm import meta_schedule as ms
import numpy as np

def compile_mod(mod, np_input_list, output_shape, output_type, opt_level=3):
with tvm.transform.PassContext(opt_level):
ref_mod = tvm.build(mod, target='llvm')
mod_output = tvm.nd.empty(output_shape, dtype=output_type, device=tvm.cpu(0))

tvm_inputs = [tvm.nd.array(x) for x in np_input_list]
ref_mod(*tvm_inputs, mod_output)
return mod_output

a = te.placeholder([1, 2, 3, 4], dtype='float32', name='a')
b = te.placeholder([4, 3, 2], dtype='float32', name='b')
op_output = topi.tensordot(a, b, axes=3)
np_inputs = [np.random.uniform(-1, 1, size=[1, 2, 3, 4]).astype('float32'),np.random.uniform(-1, 1, size=[4, 3, 2]).astype('float32')]
sch = tir.Schedule(te.create_prim_func([a, b, op_output]).with_attr('target', tvm.target.Target('llvm')))
output1 = compile_mod(sch.mod, np_inputs, op_output.shape, op_output.dtype, opt_level=3)
output2 = compile_mod(sch.mod, np_inputs, op_output.shape, op_output.dtype, opt_level=3)

np.testing.assert_allclose(
output1.numpy(), output2.numpy(), rtol=1e-3, atol=1e-3, err_msg=f"An inconsistency error."
```

### Triage

* needs-triage
* topi

Start with the provided reproducer using topi.tensordot, te.create_prim_func, tir.Schedule, and the LLVM target, then compare the generated execution across the two compile_mod calls. Trace the generated module and runtime behavior to identify why identical inputs produce different outputs. Done means repeated execution returns matching results within the stated tolerances.

Written by the indexing model from the issue text.