class_docstr: Class to build the the TF graphs for MedicalNerApproach.
  
  Examples
  --------
  
  >>> from sparknlp.training.tfgraphs import tf_graph_1x
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> feat_size = 200
  >>> n_classes = 6
  >>> tf_graph_1x.build("ner_dl", build_params={"embeddings_dim": 200, "nchars": 83,"ntags": 12},model_location="./ner_graphs",model_filename="auto")
  >>> nerTagger = NerDLApproach()    >>>                     .setInputCols(["sentence", "token", "embeddings"])    >>>                     .setLabelColumn("label")    >>>                     .setOutputCol("ner")    >>>                     .setMaxEpochs(2)    >>>                     .setBatchSize(64)    >>>                     .setRandomSeed(0)    >>>                     .setVerbose(1)    >>>                     .setValidationSplit(0.2)    >>>                     .setEvaluationLogExtended(True)     >>>                     .setEnableOutputLogs(True)    >>>                     .setIncludeConfidence(True)    >>>                     .setOutputLogsPath('ner_logs')    >>>                     .setGraphFolder('medical_ner_graphs')    >>>                     .setEnableMemoryOptimizer(True)
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code: def get_model_filename(self):
      return "blstm_{}_{}_{}_{}.pb".format(
          self.get_build_param("ntags"),
          self.get_build_param("embeddings_dim"),
          self.get_build_param("lstm_size"),
          self.get_build_param("nchars"),
      )
  
imports: import os
  import re
  from sparknlp.internal import _ResourceHelper_moveFile
  from sparknlp.training._tf_graph_builders.ner_dl.create_graph import create_graph
label_desc: Use image node_id 2 for calling the NerTFGraphBuilder obj's underlying member method code with example usage: obj.get_model_filename() and returns: str