parameters: (self, inputs, weight_collections=None, trainable=None)
return_statement: return inputs.get(self)
docstring: Returns dense `Tensor` representing numeric feature.
  
      Args:
        inputs: A `_LazyBuilder` object to access inputs.
        weight_collections: Unused `weight_collections` since no variables are
          created in this function.
        trainable: Unused `trainable` bool since no variables are created in
          this function.
  
      Returns:
        Dense `Tensor` created within `_transform_feature`.
docstring_summary: Returns dense `Tensor` representing numeric feature.
docstring_tokens: Returns dense Tensor representing numeric feature .
function: def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
      """Returns dense `Tensor` representing numeric feature.
  
      Args:
        inputs: A `_LazyBuilder` object to access inputs.
        weight_collections: Unused `weight_collections` since no variables are
          created in this function.
        trainable: Unused `trainable` bool since no variables are created in
          this function.
  
      Returns:
        Dense `Tensor` created within `_transform_feature`.
      """
      # Do nothing with weight_collections and trainable since no variables are
      # created in this function.
      del weight_collections
      del trainable
      # Feature has been already transformed. Return the intermediate
      # representation created by _transform_feature.
      return inputs.get(self)
function_tokens: def _get_dense_tensor ( self , inputs , weight_collections = None , trainable = None ) : # Do nothing with weight_collections and trainable since no variables are # created in this function. del weight_collections del trainable # Feature has been already transformed. Return the intermediate # representation created by _transform_feature. return inputs . get ( self )