[SSAConverter] Unable to determine target size for ResizeBilinear

## 🐞Describe the bug
- I am trying to convert the my_model.pb model to mlmodel using tfcoreml
- Is this a converter issue? If yes, please specify which converter (tensorflow.)

## Trace
[SSAConverter] [197/239] Converting op type: 'LeakyRelu', name: 'conv_dw_15_3_leaky_relu/LeakyRelu', output_shape: (-1, 256, -1, -1).
[SSAConverter] [198/239] Converting op type: 'Shape', name: 'up_sampling2d_1/Shape', output_shape: (4,).
[SSAConverter] [199/239] Converting op type: 'Conv2D', name: 'conv_pw_15_3/Conv2D', output_shape: (-1, 512, -1, -1).
[SSAConverter] [200/239] Converting op type: 'StridedSlice', name: 'up_sampling2d_1/strided_slice', output_shape: (2,).
[SSAConverter] [201/239] Converting op type: 'BatchNorm', name: 'conv_pw_15_3_bn/FusedBatchNormV3/Add_batch_norm', output_shape: (-1, 512, -1, -1).
[SSAConverter] [202/239] Converting op type: 'Mul', name: 'up_sampling2d_1/mul', output_shape: (2,).
[SSAConverter] [203/239] Converting op type: 'LeakyRelu', name: 'conv_pw_15_3_leaky_relu/LeakyRelu', output_shape: (-1, 512, -1, -1).
[SSAConverter] [204/239] Converting op type: 'ResizeBilinear', name: 'up_sampling2d_1/resize/ResizeBilinear', output_shape: (-1, 128, -1, -1).
Traceback (most recent call last):
File "/Users/opt/anaconda3/envs/py_374/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 58, in
class_labels='/Users/Desktop/saved_model/loto_classes.txt',
File "/Users/opt/anaconda3/envs/py_374/lib/python3.7/site-packages/tfcoreml/_tf_coreml_converter.py", line 691, in convert
custom_shape_functions=custom_shape_functions)
File "/Users/opt/anaconda3/envs/py_374/lib/python3.7/site-packages/coremltools/converters/tensorflow/_tf_converter.py", line 193, in convert
optional_inputs=optional_inputs)
File "/Users/opt/anaconda3/envs/py_374/lib/python3.7/site-packages/coremltools/converters/nnssa/coreml/ssa_converter.py", line 154, in ssa_convert
converter.convert()
File "/Users/opt/anaconda3/envs/py_374/lib/python3.7/site-packages/coremltools/converters/nnssa/coreml/ssa_converter.py", line 578, in convert
convert_func(node)
File "/Users/opt/anaconda3/envs/py_374/lib/python3.7/site-packages/coremltools/converters/nnssa/coreml/ssa_converter.py", line 2193, in _convert_resize_bilinear
raise ValueError('[SSAConverter] Unable to determine target size'
ValueError: [SSAConverter] Unable to determine target size for ResizeBilinear## To Reproduce
- If a python script can reproduce the error, please paste the code snippet
```
# Paste code snippet here
```

import tfcoreml
tfcoreml.convert(
tf_model_path='/Users/Desktop/saved_model/saved_model1.pb',
mlmodel_path='/Users/Desktop/saved_model/test.mlmodel',
input_name_shape_dict={"image_input": [1, 416, 416, 3]},
output_feature_names= ["conv2d_3/BiasAdd","conv2d_8/BiasAdd","conv2d_13/BiasAdd"],
image_input_names="image_input",
red_bias=-1,
green_bias=-1,
blue_bias=-1,
image_scale=2.0 / 255.0,
minimum_ios_deployment_target='13',
class_labels='/Users/Desktop/saved_model/classes.txt',
)

## System environment (please complete the following information):
- coremltools version (3.2):
- Tensorflow version (1.14.0)
- OS (MacOS):
- How you install python (anaconda):
- python version (3.7):
- any other relevant information:
- e.g. keras version 2.0.1
- tfcoreml version 1.1

## Additional context
We tried using coremltools 3.1 and 3.3 and also with tensorflow 1.15 and 2.2 and have the same issue .

we also tried TF OP "ResizeNearestNeighbor" which is involved by keras UpSampling2D layer with default "interpolation='nearest'" option then tried to change the interpolation method to 'bilinear' to see if it's ok for the CoreML convert tool but it didn't work as well.

Start with coremltools/converters/nnssa/coreml/ssa_converter.py, especially _convert_resize_bilinear, and reproduce the failure through tfcoreml.convert using the provided input shape and model details. Compare the ResizeBilinear trace with the converter's target-size handling; done means the reported model converts without the target-size error.

Written by the indexing model from the issue text.