Add embedding projector data to summary.
  Parameters:
     * **mat** (*torch.Tensor** or **numpy.ndarray*) -- A matrix
       which each row is the feature vector of the data point

     * **metadata** (*list*) -- A list of labels, each element
       will be convert to string

     * **label_img** (*torch.Tensor*) -- Images correspond to each
       data point

     * **global_step** (*int*) -- Global step value to record

     * **tag** (*str*) -- Name for the embedding

  Shape:
     mat: (N, D), where N is number of data and D is feature
     dimension

     label_img: (N, C, H, W)

  Examples:

     import keyword
     import torch
     meta = []
     while len(meta)<100:
         meta = meta+keyword.kwlist # get some strings
     meta = meta[:100]

     for i, v in enumerate(meta):
         meta[i] = v+str(i)

     label_img = torch.rand(100, 3, 10, 32)
     for i in range(100):
