Please add support for `torch.tensor_split` about coremltools HOT 3 OPEN

import torch
import coremltools as ct

class M(torch.nn.Module):
    def forward(self, x):
        return torch.tensor_split(x, 3)

x = torch.arange(8)
traced_model = torch.jit.trace(M(), x)
ct.convert(traced_model, inputs=[ct.TensorType(shape=x.shape)])

import coremltools as ct
import timm
import torch

model_name = "eva02_tiny_patch14_224.mim_in22k"
print(f"Creating model {model_name}")
timm_model = timm.create_model(
  model_name,
  pretrained=True,
  scriptable=False,
  exportable=True)

model = torch.nn.Sequential(
  timm_model,
  torch.nn.Softmax(1)
).eval()

input_size = timm_model.default_cfg.get("input_size")
input_shape = (1,) + input_size

print("Tracing model")
example_input = torch.randn(input_shape)
jit_model = torch.jit.trace(model, example_input)

labels_filename = "imagenet21k_wordnet_lemmas.txt"

with open(labels_filename, "r") as labels_file:
  labels = [line.strip() for line in labels_file.readlines()]

classifier_config = ct.ClassifierConfig(labels)

print("Converting model")
# Scale and bias calculations taken from Core ML Tools documentation on
# preprocessing for PyTorch
mean = list(timm_model.default_cfg.get("mean"))
std = list(timm_model.default_cfg.get("std"))
import statistics
mean_std = statistics.mean(std)
scale = 1 / (mean_std * 255)
bias = [-m / s for m, s in zip(mean, std)]
input_type = ct.ImageType(
      name="image",
      shape=input_shape,
      scale=scale,
      bias=bias)

coreml_model = ct.convert(
  jit_model,
  convert_to="mlprogram",
  inputs=[input_type],
  classifier_config=classifier_config,
  skip_model_load=True
)

coreml_model.user_defined_metadata["com.apple.coreml.model.preview.type"] = "imageClassifier"

coreml_model_file_name = f"{model_name}.mlpackage"
print(f"Saving model to {coreml_model_file_name}")

coreml_model.save(coreml_model_file_name)
print("Done!")