parameters: (self, name, input_names, output_name)
return_statement: return spec_layer
docstring: Add a where_broadcastable layer to the model that returns the elements
          either from tensor x or tensor y, depending on the value in the
          condition tensor.
          Refer to the **WhereBroadcastableLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
  
          Parameters
          ----------
          name: str
              The name of this layer.
          input_names: list of str
              The input blob names of this layer.
          output_name: str
              The output blob name of this layer.
  
          See Also
          --------
          add_where_nonzero
docstring_summary: Add a where_broadcastable layer to the model that returns the elements
          either from tensor x or tensor y, depending on the value in the
          condition tensor.
          Refer to the **WhereBroadcastableLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
docstring_tokens: Add a where_broadcastable layer to the model that returns the elements either from tensor x or tensor y depending on the value in the condition tensor . Refer to the ** WhereBroadcastableLayerParams ** message in specification ( NeuralNetwork . proto ) for more details .
function: def add_where_broadcastable(self, name, input_names, output_name):
          """
          Add a where_broadcastable layer to the model that returns the elements
          either from tensor x or tensor y, depending on the value in the
          condition tensor.
          Refer to the **WhereBroadcastableLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
  
          Parameters
          ----------
          name: str
              The name of this layer.
          input_names: list of str
              The input blob names of this layer.
          output_name: str
              The output blob name of this layer.
  
          See Also
          --------
          add_where_nonzero
          """
  
          spec_layer = self._add_generic_layer(name, input_names, [output_name])
          spec_layer.whereBroadcastable.MergeFromString(b"")
  
          self._set_max_input_rank(input_names, output_name)
  
          return spec_layer
function_tokens: def add_where_broadcastable ( self , name , input_names , output_name ) : spec_layer = self . _add_generic_layer ( name , input_names , [ output_name ] ) spec_layer . whereBroadcastable . MergeFromString ( b"" ) self . _set_max_input_rank ( input_names , output_name ) return spec_layer