Batch Inference does not work when using the default handler

**Describe the bug**
1. In batch inference, the model-server (in this case, torchserve) will return a 'batch' i.e list of requests to the handler. The handler is expected to process them and send back the responses. This would be a list of 'batch-size' responses.
2. Currently, the pt toolkit uses the transform() function from the base inference-toolkit to receive requests from the model server, and process them by calling the _transform_fn() i.e [which calls _input_fn, _predict_fn, _output_fn].
3. However, it seems to only process the 'first' request in the batch: https://github.com/aws/sagemaker-inference-toolkit/blob/master/src/sagemaker_inference/transformer.py#L114
4. When using the default handler, all but the first requests get dropped.
5. This restricts using the default handler for batch inference.

**To reproduce**
A clear, step-by-step set of instructions to reproduce the bug:
1. A run-through of this notebook: https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-python-sdk/pytorch_batch_inference/sagemaker_batch_inference_torchserve.ipynb results in the failure (log attached in respective section).
[This notebook has a workaround PR using custom container: https://github.com/aws/amazon-sagemaker-examples/pull/3395]

**Expected behavior**
1. The transform() function should process all requests in a batch, and return a list of responses which equals in size to the input list.
2. Attaching an untested suggestion in the form of a PR: [link](https://github.com/aws/sagemaker-pytorch-inference-toolkit/pull/122)

**Screenshots or logs**
1. A run of the notebook results in:
```
2022-04-27T20:51:36,026 [INFO ] W-9000-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req. to backend at: 1651092696026
2022-04-27T20:51:36,028 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Backend received inference at: 1651092696
2022-04-27T20:51:36,028 [WARN ] W-9000-model_1.0-stderr MODEL_LOG - Downloading: 100%|██████████| 28.0/28.0 [00:00<00:00, 42.8kB/s]
2022-04-27T20:51:36,028 [WARN ] W-9000-model_1.0-stderr MODEL_LOG - Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
2022-04-27T20:51:36,119 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - INPUT1
2022-04-27T20:51:36,120 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - INPUT2
2022-04-27T20:51:36,120 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Got input Data: {Bloomberg has decided to publish a new report on global economic situation.}
2022-04-27T20:51:36,120 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - PRED SequenceClassifierOutput(loss=None, logits=tensor([[ 0.1999, -0.2964]], grad_fn=), hidden_states=None, attentions=None)
2022-04-27T20:51:36,120 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - PREDICTION ['Not Accepted']
2022-04-27T20:51:36,120 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - model: model, number of batch response mismatched, expect: 3, got: 1.
2022-04-27T20:51:36,121 [INFO ] W-9000-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 94
```

**System information**
A description of your system. Please provide:
- **Toolkit version**:
- **Framework version**:
- **Python version**: py38
- **CPU or GPU**: CPU
- **Custom Docker image (Y/N)**: N, DLC:

**Additional context**
Add any other context about the problem here.

Start with transform() in sagemaker_inference/transformer.py around line 114 and reproduce the issue using the linked PyTorch batch-inference notebook. Review the linked PR 122 and verify that batch processing returns one response for each input request, including tests that cover the reported mismatch.

Written by the indexing model from the issue text.