The loading and saving of a simple model with a Concatenate layer is tested to check if the model is the same after reloading.
Implemented missing axis parameter for np.stack (added some handy tuple calls too like the np.concatenate example).

model.load_model now supports loading of concatenate layers.
python tensorflow exports concatenate layers in an extra nested array in the manifest so added a check for that in generic_utils.cs.
Concatenate was missing the build=true, this fix prevents the layer being build multiple times.
Concatenate has 2 or more input nodes so List<NodeConfig> was required instead of just NodeConfig in Functional.FromConfig.cs.
Added missing axis JsonProperty attribute for MergeArgs (used by Concatenate)

解决keras模式下，使用GPU训练时会爆显存的bug。

观察到的现象是，一些模型增大batchsize后，会在首个epoch的中途爆显存不足，只要过了一个epoch后，就能完整训练。同样的batchsize在python下能设置大得多的值。
最后使用最小训练代码分析出，是每个step之后，图片加载到显存里的数据没有释放导致的。
在寻找释放显存接口没有结果的时候，直接使用了GC.Collect();可以让显存主动回收。
因此当前的修复方案是在每个step里，都执行一次 GC.Collect(); 用来释放显存资源。

feat: add the implementation of class_weight in model.fit

Allow Model to cache weights.

feat: add the implementation of sample_weight in model.fit

Improve RaggedTensor

fix: adjust imdb dataset loader for faster loading speed

fix: fix EarlyStopping

fix: improve unpickler speed with BufferedStream

optimize: temporal complexity of Imdb dataset loader

Gradient tests which were dependant only on cached_session

cached_session for graph tests

fix: model does not stop on stop_training == true #1170

add: loading pickled npy file for imdb dataset loader

# Conflicts:
#	src/TensorFlowNET.Core/Tensorflow.Binding.csproj
#	src/TensorFlowNET.Keras/Datasets/Imdb.cs

feat: implement GRU layer

fix: validate dataset of `Imdb` do not load bug & add: custom `Imdb` path

fix: partially fix the bug of load_model