(self, tag)
return nc_hist
Compute a histogram of the current tensor values.

        Arguments:
            tag (string): Tag to identify the current state of the tensor,
                          useful for disambiguating multiple histograms of the
                          same tensor at different points in time.

        Returns:
            Tensor containing the histogram data.
Compute a histogram of the current tensor values.
Compute a histogram of the current tensor values .
def hist(self, tag):
        """
        Compute a histogram of the current tensor values.

        Arguments:
            tag (string): Tag to identify the current state of the tensor,
                          useful for disambiguating multiple histograms of the
                          same tensor at different points in time.

        Returns:
            Tensor containing the histogram data.

        """
        nbins = self.backend.hist_bins
        offset = self.backend.hist_offset
        bins = np.arange(nbins + 1) + float(offset)
        bins[0] = -float('Inf')
        np_inp_log_abs = np.rint(
            np.log2(np.abs(self._tensor.astype(np.float32))))
        np_hist, edges = np.histogram(np_inp_log_abs, density=False, bins=bins)
        nc_hist = self.backend._hist_tensor(tag)._assign(np_hist)
        return nc_hist
def hist ( self , tag ) : nbins = self . backend . hist_bins offset = self . backend . hist_offset bins = np . arange ( nbins + 1 ) + float ( offset ) bins [ 0 ] = - float ( 'Inf' ) np_inp_log_abs = np . rint ( np . log2 ( np . abs ( self . _tensor . astype ( np . float32 ) ) ) ) np_hist , edges = np . histogram ( np_inp_log_abs , density = False , bins = bins ) nc_hist = self . backend . _hist_tensor ( tag ) . _assign ( np_hist ) return nc_hist