Configure the model layers

This is a guide on configuring the model's input arguments.
Called the model_config, examples can be found inside the misc folder.
To use this, you need to provide the path of the local model_config in the config_*_*.yaml under the training subsection.
Example:
- In config_timeseries_classification_dsi.yaml
- Under training section:

training:
    enable: True
    model_name: 'TimeSeries_Generic_13k_t'
    model_config: '/home/a/b/tinyml-modelmaker/misc/TimeSeries_Generic_x_t.yaml'

The contents of the yaml file needs to have the parameters that the model accepts as an input argument
For example, the class definition of CNN_TS_GEN_BASE_13K (which is referred to by TimeSeries_Generic_13k_t) contains the following input arguments:
- input_features, variables, num_classes, with_input_batchnorm

class CNN_TS_GEN_BASE_13K(GenericModelWithSpec):
    def __init__(self, config, input_features=512, variables=1, num_classes=2, with_input_batchnorm=True):

So we can have (none or upto) the following arguments in /home/a/b/tinyml-modelmaker/misc/TimeSeries_Generic_13k_t.yaml

input_features: 512
variables: 2
num_classes: 3
with_input_batchnorm: False

This will edit the model properties accordingly
Kindly see more examples in misc folder.