I use start_dim=2 based on the dimensions of the MNIST data.
The data is brought in as follows:
# Fashion-MNIST dataset
train_set = torchvision.datasets.FashionMNIST(
	root = './data/FashionMNIST',
	train = True,
	download = True,
	transform = transforms.Compose([
		transforms.ToTensor()                                 
	])
)

test_set = torchvision.datasets.FashionMNIST(
	root = './data/FashionMNIST',
	train = False,
	download = True,
	transform = transforms.Compose([
		transforms.ToTensor()                                 
	])
)

Then performing this:
test_loader = torch.utils.data.DataLoader(dataset)
for data, target in test_loader:
			print("Init shape: {}, nreshape: {}, length: {}, target shape: {}".format(
				data.shape, data.flatten(start_dim=2).shape, len(test_loader.dataset), target.shape))

Yields this:
Init shape: torch.Size([1, 1, 28, 28]), nreshape: torch.Size([1, 1, 784]), length: 10000, target shape: torch.Size([1])

Do you still think the start_dim=1 would be helpful?
Thanks