The model must be traced in PyTorch first before importing into MATLAB. Please see PyTorch documentation fo some details on how it's done. https://pytorch.org/docs/stable/generated/torch.jit.trace.html
You can also read this blog post for additional information: https://blogs.mathworks.com/deep-learning/2022/10/04/whats-new-in-interoperability-with-tensorflow-and-pytorch/
As a simple example, try something similar to the following in PyTorch:
# This example loads a pretrained PyTorch model from torchvision,
# traces it with example inputs, and saves the trace as a .pt file.
import torch
from torchvision import models
# Load the model with pretrained weights
model = models.mobilenet_v2(pretrained=True)
# Call "eval" to ensure that layers like batch norm and dropout are set to
# inference mode
model.eval()
# Move the model to the CPU
model.to("cpu")
# Create example inputs
X = torch.rand(1, 3, 224, 224)
# Trace model with the example input
traced_model = torch.jit.trace(model.forward, X)
# Save the traced model to a .pt file
traced_model.save('traced_mnasnet.pt')
