[docs] Fix fp16 mixed precision example to reflect correct input variable names

docs/performance/model-optimizations/float16.md

@@ -62,7 +62,9 @@ from onnxconverter_common import auto_mixed_precision
 import onnx
 
 model = onnx.load("path/to/model.onnx")
-model_fp16 = auto_convert_mixed_precision(model, test_data, rtol=0.01, atol=0.001, keep_io_types=True)
+# Assuming x is the input to the model
+feed_dict = {'input': x.numpy()}
+model_fp16 = auto_convert_mixed_precision(model, feed_dict, rtol=0.01, atol=0.001, keep_io_types=True)
 onnx.save(model_fp16, "path/to/model_fp16.onnx")
 ```
 
@@ -73,6 +75,7 @@ auto_convert_mixed_precision(model, feed_dict, validate_fn=None, rtol=None, atol
 ```
 
 - `model`: The ONNX model to convert.
+- `feed_dict`: Test data used to measure the accuracy of the model during conversion. Format is similar to InferenceSession.run (map of input names to values)
 - `validate_fn`: A function accepting two lists of numpy arrays (the outputs of the float32 model and the mixed-precision model, respectively) that returns `True` if the results are sufficiently close and `False` otherwise. Can be used instead of or in addition to `rtol` and `atol`.
 - `rtol`, `atol`: Absolute and relative tolerances used for validation. See [numpy.allclose](https://numpy.org/doc/stable/reference/generated/numpy.allclose.html) for more information.
 - `keep_io_types`: Whether model inputs/outputs should be left as float32.