parameters: (self, name, input_names, output_name, start=0, step=1)
return_statement: return spec_layer
docstring: Add a range_dynamic layer that returns a tensor that contains evenly spaced values.
          This layer has up to three inputs or no input and three parameters.
          Refer to the **RangeDynamicLayerParams** 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.
              If input size == 1: end is input, start and step are read from parameters
              If input size == 2: end, start are inputs, step is read from parameters
              If input size == 3: start, end, step are all inputs, none of the parameters are used.
          output_name: str
              The output blob name of this layer.
          start: int, optional
              Range parameter: start. Ignored if start is provided as input, default: 0.
          step: int, optional
              Range parameter: step. Ignored if step is provided as input, default: 1.
  
          See Also
          --------
          add_range_static
docstring_summary: Add a range_dynamic layer that returns a tensor that contains evenly spaced values.
          This layer has up to three inputs or no input and three parameters.
          Refer to the **RangeDynamicLayerParams** message in specification (NeuralNetwork.proto) for more details.
docstring_tokens: Add a range_dynamic layer that returns a tensor that contains evenly spaced values . This layer has up to three inputs or no input and three parameters . Refer to the ** RangeDynamicLayerParams ** message in specification ( NeuralNetwork . proto ) for more details .
function: def add_range_dynamic(self, name, input_names, output_name, start=0, step=1):
          """
          Add a range_dynamic layer that returns a tensor that contains evenly spaced values.
          This layer has up to three inputs or no input and three parameters.
          Refer to the **RangeDynamicLayerParams** 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.
              If input size == 1: end is input, start and step are read from parameters
              If input size == 2: end, start are inputs, step is read from parameters
              If input size == 3: start, end, step are all inputs, none of the parameters are used.
          output_name: str
              The output blob name of this layer.
          start: int, optional
              Range parameter: start. Ignored if start is provided as input, default: 0.
          step: int, optional
              Range parameter: step. Ignored if step is provided as input, default: 1.
  
          See Also
          --------
          add_range_static
          """
  
          if len(input_names) < 1 or len(input_names) > 3:
              raise ValueError("RangeDynamic layer must have either 1, 2 or 3 inputs.")
  
          spec_layer = self._add_generic_layer(name, input_names, [output_name])
          spec_layer.rangeDynamic.MergeFromString(b"")
          spec_params = spec_layer.rangeDynamic
  
          spec_params.startValue = float(start)
          spec_params.stepSizeValue = float(step)
  
          self.rank_dict[output_name] = 1
          return spec_layer
function_tokens: def add_range_dynamic ( self , name , input_names , output_name , start = 0 , step = 1 ) : if len ( input_names ) < 1 or len ( input_names ) > 3 : raise ValueError ( "RangeDynamic layer must have either 1, 2 or 3 inputs." ) spec_layer = self . _add_generic_layer ( name , input_names , [ output_name ] ) spec_layer . rangeDynamic . MergeFromString ( b"" ) spec_params = spec_layer . rangeDynamic spec_params . startValue = float ( start ) spec_params . stepSizeValue = float ( step ) self . rank_dict [ output_name ] = 1 return spec_layer