class_docstr: Dump a tensor to image(s) to ``logger.get_logger_dir()`` once triggered.
  
  Note that it requires the tensor is directly evaluable, i.e. either inputs
  are not its dependency (e.g. the weights of the model), or the inputs are
  feedfree (in which case this callback will take an extra datapoint from the input pipeline).
class_docstr_tok: ["Dump","a","tensor","to","image","(","s",")","to","``","logger.get_logger_dir","(",")","``","once","triggered",".","Note","that","it","requires","the","tensor","is","directly","evaluable",",","i.e",".","either","inputs","are","not","its","dependency","(","e.g",".","the","weights","of","the","model",")",",","or","the","inputs","are","feedfree","(","in","which","case","this","callback","will","take","an","extra","datapoint","from","the","input","pipeline",")","."]
code: def _before_train(self):
      self._tensor = self.graph.get_tensor_by_name(self.tensor_name)
  
imports: import numpy
  import os
  from ..compat import tfv1
  from ..tfutils.common import get_op_tensor_name
  from ..utils import logger
  from .base import Callback
label_desc: Use image node_id 2 for calling the DumpTensorAsImage obj's underlying member method code with example usage: obj._before_train() without return types