Given Shapes are not Broadcastable, tf to tf-lite conversion error
I think the bug is more that Resizing is converted to a broadcasting operation. If we remove it and make the shapes equivalent, I am able to convert: [gist](https://colab.sandbox.google.com/gist/pkgoogle/d3c0c4c6459343a0a5f7de1d0305db0f/conversionfails.ipynb)

Hi @arfaian, can you please take a look? Thanks. Thanks for taking a look, @pkgoogle  and @arfaian . If I can do anything, let me know. 
### 1. System information

Google colab as of 2024-02-27
tf version: 2.15.0

### Problem
I receive the following error when converting a tf module from tf to tfl-lite:
```
RuntimeError: Given shapes, [1,436,1024,3] and [1,218,512,3], are not broadcastable.Node number 1 (SUB) failed to prepare.
```
Below is the relevant class:
```
class Inpainting(Model):
    def __init__(self, downsample_factor, **kwargs):
        super(Inpainting, self).__init__(**kwargs)
        self.downsample_factor = downsample_factor
        self.downsize = Resizing(218, 512)

    @tf.function(input_signature=[
        tf.TensorSpec(shape=(1, 218, 512, 3)),
        tf.TensorSpec(shape=(1, 218, 512, 1)),
        tf.TensorSpec(shape=(1, 436, 1024, 3)),
        tf.TensorSpec(shape=(1, 436, 1024, 1)),
        ])
    def call(self, img_t_lr, depth_t_lr, img_t_w, depth_t_w):

        img_t_wlr = self.downsize(img_t_w)
        depth_t_wlr = self.downsize(depth_t_w)
        assert img_t_wlr.shape[1:] == img_t_lr.shape[1:], "must be same shape"
        diff_color = tf.math.subtract(img_t_lr, img_t_wlr)
        assert depth_t_lr.shape[1:] == depth_t_wlr.shape[1:], "must be same shape"
        diff_depth = tf.math.subtract(depth_t_lr, depth_t_wlr)
        diff = tf.concat([diff_color, diff_depth], axis=3)
        return diff
```
Please see here for the full [gist](https://colab.research.google.com/drive/18Q2-NvHFre1-1Dk2HYjTjX1MnUveERle?usp=sharing). 

I am a bit surprised that this happens. Could this be a bug? 
