Dynamic batch sizes are not supported in tiny-yolov3 model

# Bug Report ### Which model does this pertain to? Tiny-yolov3 ### Describe the bug Model does not support batch sizes > 1. error : ----------- testing start ---------- input data name : input_1, shape = (2, 3, 416, 416), type = <class 'numpy.ndarray'> input data name : image_shape, shape = (2, 2), type = <class 'numpy.ndarray'> 2025-03-19 23:19:18.958568678 [E:onnxruntime:, sequential_executor.cc:516 ExecuteKernel] Non-zero status code returned while running Squeeze node. Name:'TFNodes/yolo_evaluation_layer_1/Squeeze' Status Message: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.h:52 static onnxruntime::TensorShapeVector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShapeVector&) input_shape[i] == 1 was false. Dimension of input 0 must be 1 instead of 2. shape={2,2} ### Reproduction instructions Run model inference on any batch size > 1. **System Information** OS Platform and Distribution (*e.g. Linux Ubuntu 16.04*): ONNX version (*e.g. 1.6*): Backend/Runtime version (*e.g. ONNX Runtime 1.1, PyTorch 1.2*): Provide a code snippet to reproduce your errors. ``` import numpy as np def test_call(self, ): for batch_size in [1, 2]: image = np.random.rand(batch_size, 3, 416, 416,).astype(np.float32) image_shape = np.array([[416, 416]], dtype=np.float32).reshape(1, 2) if batch_size != 1: image_shape = np.vstack([ image_shape for _ in range(batch_size)]) inputs = { self.input_names[0]: image, self.input_names[1]: image_shape } print(f"----------- testing start ----------") for key, value in inputs.items(): print(f"input data name : {key}, shape = {value.shape}, type = {type(value)}") # any forward wrapper that takes in the input and returns the output dict. output_dict = self.forward(inputs) for key, value in output_dict.items(): print(f"output data name : {key}, shape = {value.shape}, type = {type(value)}") ... ``` ### Notes 1. runs correct with batch size 1. 2. fails on any batch size > 1