Problems to use a converted Py Torch Vanilla NN in a Pipeline when it has hidden layers with different shapes from input layer

## 🐞Describing the bug
- I have create a PyTorch very simple vanilla neural network:
`````python
class SimpleModel(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(3, 4)
#self.activation1 = nn.ReLU()
#self.linear2 = nn.Linear(4, 3)
self.linear3 = nn.Linear(4, 1)

def forward(self, x):
x = self.linear1(x)
#x = self.activation1(x)
#x = self.linear2(x)
x = self.linear3(x)
return x
``````

And converted it to a Core ML package using Core ML Tools

`````python
input = torch.rand(1,3)
model.eval()
traced_model = torch.jit.trace(model, input)
mlmodel = ct.convert(
traced_model,
inputs=[ct.TensorType(name="input", shape=input.shape)],
)
mlmodel.save("Test.mlpackage")
``````

This model can be open in Xcode with no problem

But, if I try to use it in a Pipeline:

`````python
pipeline_network = pipeline.Pipeline (
input_features = [("input",datatypes.Array(1,3))],
output_features=[("linear_1",datatypes.Array(1,1))]
)
pipeline_network.add_model(mlmodel)
pipeline_spec = pipeline_network.spec
ct.utils.convert_double_to_float_multiarray_type(pipeline_spec)
ct.utils.save_spec(pipeline_spec, "Test-Pipeline.mlpackage")
``````
It does not open on XCode and give me the following error:

This problem does not occur if `````self.linear1 = nn.Linear(3, 3)````` and `````self.linear3 = nn.Linear(3, 1)`````.

## To Reproduce
- Here is my full example:
```python

!pip install torch==2.1.2
!pip install --upgrade coremltools

!rm -Rf /content/Test.mlpackage
!rm -Rf /content/Test-Pipeline.mlpackage

!rm -Rf /content/simple-export.zip
!rm -Rf /content/pipeline-export.zip

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(3, 4) #changing 4 to 3 here and in linear3 make it work
#self.activation1 = nn.ReLU()
#self.linear2 = nn.Linear(4, 3)
self.linear3 = nn.Linear(4, 1) #changing 4 to 3 here and in linear1 make it work

def forward(self, x):
x = self.linear1(x)
#x = self.activation1(x)
#x = self.linear2(x)
x = self.linear3(x)
return x

input = torch.rand(1,3)

model = SimpleModel()

model.eval()

traced_model = torch.jit.trace(model, input)

import coremltools as ct
import numpy as np

mlmodel = ct.convert(
traced_model,
inputs=[ct.TensorType(name="input", shape=input.shape)],
outputs=[ct.TensorType(name="linear_1")],
compute_precision = ct.precision.FLOAT32,
compute_units=ct.ComputeUnit.CPU_AND_GPU
#minimum_deployment_target = ct.target.iOS17,
#convert_to="mlprogram"
)

mlmodel.save("Test.mlpackage")

!zip -r /content/simple-export.zip /content/Test.mlpackage

from coremltools.models import pipeline
from coremltools.models import datatypes

import coremltools.models as models

pipeline_network = pipeline.Pipeline (
input_features = [("input",datatypes.Array(1,3))],
output_features=[("linear_1",datatypes.Array(1,1))]
)

pipeline_network.add_model(mlmodel)

pipeline_spec = pipeline_network.spec

ct.utils.convert_double_to_float_multiarray_type(pipeline_spec)

ct.utils.save_spec(pipeline_spec, "Test-Pipeline.mlpackage")

!zip -r /content/pipeline-export.zip /content/Test-Pipeline.mlpackage

```

Test.mlpackage will open without problem in Xcode, but Test-Pipeline.mlpackage will present the outputSchema error...

## System environment
- coremltools version: 8.1
- Colab environment
- Py torch version: 2.1.2

Start by running the provided PyTorch and coremltools 8.1 reproduction, focusing on pipeline.Pipeline.add_model and the outputSchema error reported for Test-Pipeline.mlpackage. Compare the working standalone Test.mlpackage with the pipeline package and verify that the corrected pipeline package opens in Xcode when the hidden layer shapes differ.

Written by the indexing model from the issue text.