[Bug] How to migrate from te.create_schedule and auto_scheduler to TVM v0.20’

I am using the release of v0.20. migrating from V0.19. The following function seems to have been deprecated. Can you show me a migration guide or functions that can replace it? Thanks

---
AttributeError: module 'tvm.te' has no attribute 'create_schedule'
```
s = te.create_schedule(C.op)
```

---
ImportError: cannot import name 'auto_scheduler' from 'tvm' (/home/doc/tvm/python/tvm/__init__.py). Did you mean: 'meta_schedule'?
```
from tvm import te, auto_scheduler
```
---
The simple TVM code that work with v0.19 are:
```
import tvm
from tvm import te

import numpy as np

# Define the computation
n = te.var("n") # symbolic variable
A = te.placeholder((n,), name="A")
B = te.placeholder((n,), name="B")
C = te.compute((n,), lambda i: A[i] + B[i], name="C")

# Schedule the computation
s = te.create_schedule(C.op)

# Build the function
fadd = tvm.build(s, [A, B, C], target="llvm", name="vector_add")

# Prepare input data
n_val = 8
a_np = np.random.uniform(size=n_val).astype("float32")
b_np = np.random.uniform(size=n_val).astype("float32")
c_np = np.zeros(n_val, dtype="float32")

# Allocate TVM buffers
ctx = tvm.cpu()
a_tvm = tvm.nd.array(a_np, ctx)
b_tvm = tvm.nd.array(b_np, ctx)
c_tvm = tvm.nd.array(c_np, ctx)

# Run the function
fadd(a_tvm, b_tvm, c_tvm)

# Validate correctness
np.testing.assert_allclose(c_tvm.asnumpy(), a_np + b_np)
print("Success! Output:", c_tvm.asnumpy())
```

Start from the v0.19 Python example and the reported entry points `te.create_schedule`, `tvm.auto_scheduler`, and `meta_schedule`. Verify the v0.20 replacements and document a migration path for both scheduling and auto-scheduling, including an updated vector-add example. Done means the guide explains the replacement APIs clearly enough to resolve both reported errors.

Written by the indexing model from the issue text.