parameters: (self, input_tensors, **kwargs)
return_statement: return self.return_type(ops.SparseTensor(sparse_indices, values, shape))
docstring: Applies the transformation to the `transform_input`.
  
      Args:
        input_tensors: a list of Tensors representing the input to
          the Transform.
        **kwargs: Additional keyword arguments, unused here.
  
      Returns:
          A namedtuple of Tensors representing the transformed output.
docstring_summary: Applies the transformation to the `transform_input`.
docstring_tokens: Applies the transformation to the transform_input .
function: def _apply_transform(self, input_tensors, **kwargs):
      """Applies the transformation to the `transform_input`.
  
      Args:
        input_tensors: a list of Tensors representing the input to
          the Transform.
        **kwargs: Additional keyword arguments, unused here.
  
      Returns:
          A namedtuple of Tensors representing the transformed output.
      """
      d = input_tensors[0]
  
      if self.strip_value is np.nan:
        strip_hot = math_ops.is_nan(d)
      else:
        strip_hot = math_ops.equal(d,
                                   array_ops.constant([self.strip_value],
                                                      dtype=d.dtype))
      keep_hot = math_ops.logical_not(strip_hot)
  
      length = array_ops.reshape(array_ops.shape(d), [])
      indices = array_ops.boolean_mask(math_ops.range(length), keep_hot)
      values = array_ops.boolean_mask(d, keep_hot)
  
      sparse_indices = array_ops.reshape(
          math_ops.cast(indices, dtypes.int64), [-1, 1])
      shape = math_ops.cast(array_ops.shape(d), dtypes.int64)
  
      # pylint: disable=not-callable
      return self.return_type(ops.SparseTensor(sparse_indices, values, shape))
function_tokens: def _apply_transform ( self , input_tensors , * * kwargs ) : d = input_tensors [ 0 ] if self . strip_value is np . nan : strip_hot = math_ops . is_nan ( d ) else : strip_hot = math_ops . equal ( d , array_ops . constant ( [ self . strip_value ] , dtype = d . dtype ) ) keep_hot = math_ops . logical_not ( strip_hot ) length = array_ops . reshape ( array_ops . shape ( d ) , [ ] ) indices = array_ops . boolean_mask ( math_ops . range ( length ) , keep_hot ) values = array_ops . boolean_mask ( d , keep_hot ) sparse_indices = array_ops . reshape ( math_ops . cast ( indices , dtypes . int64 ) , [ - 1 , 1 ] ) shape = math_ops . cast ( array_ops . shape ( d ) , dtypes . int64 ) # pylint: disable=not-callable return self . return_type ( ops . SparseTensor ( sparse_indices , values , shape ) )