parameters: (
          self, name, input_name, output_name, axes=None, keepdims=True, reduce_all=False
      )
return_statement: return spec_layer
docstring: Add a reduce_max layer to the model that reduces the input tensor
          using ``max(elements across given dimensions)``.
          Refer to the **ReduceMaxLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
  
          Parameters
          ----------
          name: str
              The name of this layer.
          input_name: str
              The input blob name of this layer.
          output_name: str
              The output blob name of this layer.
          axes: list of int or tuple of int, optional
              List of dimensions for the reduce operations.
              Each should be in range [-rank(input), rank(input)), default: None (reduce_all)
          keepdims: bool, optional
              Whether or not to retain the reduced dimensions with length 1, default: true.
          reduce_all: bool, optional
              Whether or not to reduce on all axes, default: false.
  
          See Also
          --------
          add_reduce_l1, add_reduce_l2, add_reduce_sum, add_reduce_min, add_reduce_prod
          add_reduce_mean, add_reduce_logsum, add_reduce_logsumexp, add_reduce_sumsquare
docstring_summary: Add a reduce_max layer to the model that reduces the input tensor
          using ``max(elements across given dimensions)``.
          Refer to the **ReduceMaxLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
docstring_tokens: Add a reduce_max layer to the model that reduces the input tensor using max ( elements across given dimensions ) . Refer to the ** ReduceMaxLayerParams ** message in specification ( NeuralNetwork . proto ) for more details .
function: def add_reduce_max(
          self, name, input_name, output_name, axes=None, keepdims=True, reduce_all=False
      ):
          """
          Add a reduce_max layer to the model that reduces the input tensor
          using ``max(elements across given dimensions)``.
          Refer to the **ReduceMaxLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
  
          Parameters
          ----------
          name: str
              The name of this layer.
          input_name: str
              The input blob name of this layer.
          output_name: str
              The output blob name of this layer.
          axes: list of int or tuple of int, optional
              List of dimensions for the reduce operations.
              Each should be in range [-rank(input), rank(input)), default: None (reduce_all)
          keepdims: bool, optional
              Whether or not to retain the reduced dimensions with length 1, default: true.
          reduce_all: bool, optional
              Whether or not to reduce on all axes, default: false.
  
          See Also
          --------
          add_reduce_l1, add_reduce_l2, add_reduce_sum, add_reduce_min, add_reduce_prod
          add_reduce_mean, add_reduce_logsum, add_reduce_logsumexp, add_reduce_sumsquare
          """
  
          spec_layer = self._add_generic_layer(name, [input_name], [output_name])
          spec_layer_params = spec_layer.reduceMax
  
          if axes is not None and len(axes) != 0:
              spec_layer_params.axes.extend(map(int, axes))
          else:
              reduce_all = True
  
          spec_layer_params.keepDims = keepdims
          spec_layer_params.reduceAll = reduce_all
  
          self._set_rank_for_reduce_op(
              input_name, output_name, axes, keepdims, reduce_all
          )
          return spec_layer
function_tokens: def add_reduce_max ( self , name , input_name , output_name , axes = None , keepdims = True , reduce_all = False ) : spec_layer = self . _add_generic_layer ( name , [ input_name ] , [ output_name ] ) spec_layer_params = spec_layer . reduceMax if axes is not None and len ( axes ) != 0 : spec_layer_params . axes . extend ( map ( int , axes ) ) else : reduce_all = True spec_layer_params . keepDims = keepdims spec_layer_params . reduceAll = reduce_all self . _set_rank_for_reduce_op ( input_name , output_name , axes , keepdims , reduce_all ) return spec_layer