class_docstr: Class providing an artificial backend, that reads data into memory.
class_docstr_tok: ["Class","providing","an","artificial","backend",",","that","reads","data","into","memory","."]
docstr: Get the output signature for the tf.data.Dataset object.
docstr_tok: ["Get","the","output","signature","for","the","tf.data.Dataset","object","."]
code: def get_output_signature(self):
      """Get the output signature for the tf.data.Dataset object."""
      triple_tensor = tf.TensorSpec(shape=(None, 3), dtype=tf.int32)
      if self.data_shape > 3:
          weights_tensor = tf.TensorSpec(
              shape=(None, self.data_shape - 3), dtype=tf.float32
          )
          if self.use_filter:
              return (
                  triple_tensor,
                  tf.RaggedTensorSpec(
                      shape=(2, None, None), dtype=tf.int32
                  ),
                  weights_tensor,
              )
          else:
              return (triple_tensor, weights_tensor)
      if self.use_filter:
          return (
              triple_tensor,
              tf.RaggedTensorSpec(
                  shape=(2, None, None), dtype=tf.int32
              ),
          )
      return triple_tensor
  
imports: import logging
  import tempfile
  import uuid
  from datetime import datetime
  import numpy
  import pandas
  import tensorflow
  from .data_indexer import DataIndexer
  from .source_identifier import DataSourceIdentifier
label_desc: Use image node_id 5 for calling the NoBackend obj's underlying member method code with example usage: obj.get_output_signature() and returns: triple_tensor, triple_tensor,  tf, triple_tensor,  tf,  weights_tensor, triple_tensor,  weights_tensor