We will add Var32 Tensor if the Variable Weight is not Full
precision (FP32). This eables the Weight Update with full precision
and only Apply Gradient Process ueses this Tensor. Therefore, the
lifespan of this tensor should be "ApplyGradient".

. Modify TensorPool to generate Weigth considering Mixed Precsion.

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This pr create the variable fp32 tensor when we create the Weight and
Optimizer Weight.

. update the manager to create Weight with  var32 tensor which
requested to weight pool.
. update the weight requests with Weight Spec and var, grad and var32
tensors which created already.
. add clone Tensor with specific type in tensor.h

Resolves:

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This PR enables the FP16 support for the layers below:

. input layer
. mse loss layer

Resolves:

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This PR includes the mixed precision test case.

. Input - FC - MSE
 : "batch_size=2", "model_tensor_type=FP16-FP16", "loss_scale=128"

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This commit modify apply gradient in optimizer.
We do not need to save optimizer variables in weight type. Only
Optimizer needs the optimizer variables and we should update the
weight with full precision to maintain the accuracy. Therefore,
remove the var32 tensors for optimizer variables.

Resolves:

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This PR add is_NaN function to check if the tensor has NaN value. This
is for the check NaN during mixed precision training.

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This PR add loss scale parameter in runcontext and use it to update
mse loss.

. Add Loss Scale Parameter in RunLayerContext Constructor
. Add applyLossScale func to update return derivitive in Loss Layer
. Change MSE Loss Layer to apply the loss scale to return derivitive

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