NotImplementedError: Only tensor assignment with exactly 1 pure dimension selection is supported

## 🌱 Describe your Feature Request
Coremltools 5.2 only supports tensor assignment with exactly 1 pure dimension selection. I'm trying to convert a model from pytorch to coreml, which involves more flexible tensor assignments, as in the use case. After running the code, it raises a NotImplementedError("Only tensor assignment with exactly 1 pure dimension selection is supported"). It would be great if the feature could be supported.

## Use case
```
import torch
import torch.nn as nn
import coremltools as ct

class Model(nn.Module):

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

def forward(self, x):
x[:, :4, :-1, :-1] = 0
return x

if __name__ == "__main__":
x = torch.randn(1, 3, 8, 8)
model = Model()
model.eval()
y = model(x)

torchscript_model = torch.jit.trace(model, x)
coreml_model = ct.convert(
torchscript_model,
inputs=[ct.ImageType(name="x_1",
shape=x.shape)]
)
```

Reproduce the provided Model and torch.jit.trace example, then follow the PyTorch-to-Core ML conversion through ct.convert. Locate the conversion path that raises the stated NotImplementedError and identify the existing tensor-assignment handling; done means the example converts without that error while preserving the assignment behavior.

Written by the indexing model from the issue text.