parameters: (x, y, cond=None, name=None)
return_statement: return cond
docstring: :alias_main: paddle.greater_equal
  	:alias: paddle.greater_equal,paddle.tensor.greater_equal,paddle.tensor.logic.greater_equal
  	:old_api: paddle.fluid.layers.greater_equal
  
      This OP returns the truth value of :math:`x >= y` elementwise, which is equivalent function to the overloaded operator `>=`.
  
      Args:
          x(Variable): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64. 
          y(Variable): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
          cond(Variable, optional): Optional output which can be any created Variable that meets the requirements to store the result of *greater_equal*.
              if cond is None, a new Varibale will be created to store the result.
          name(str, optional): The default value is None.  Normally there is no need for
              user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          Variable, the output data type is bool: The tensor variable storing the output, the output shape is same as input :attr:`x`.
  
      Examples:
          .. code-block:: python
  
            import paddle.fluid as fluid
            import numpy as np
  
            label = fluid.layers.assign(np.array([2, 2], dtype='int32'))
            limit = fluid.layers.assign(np.array([2, 3], dtype='int32'))
            out = fluid.layers.greater_equal(x=label, y=limit) #out=[True, False]
            out_1 = label >= limit #out1=[True, False]
docstring_summary: :alias_main: paddle.greater_equal
  	:alias: paddle.greater_equal,paddle.tensor.greater_equal,paddle.tensor.logic.greater_equal
  	:old_api: paddle.fluid.layers.greater_equal
docstring_tokens: : alias_main : paddle . greater_equal : alias : paddle . greater_equal paddle . tensor . greater_equal paddle . tensor . logic . greater_equal : old_api : paddle . fluid . layers . greater_equal
function: def greater_equal(x, y, cond=None, name=None):
      """
      :alias_main: paddle.greater_equal
  	:alias: paddle.greater_equal,paddle.tensor.greater_equal,paddle.tensor.logic.greater_equal
  	:old_api: paddle.fluid.layers.greater_equal
  
      This OP returns the truth value of :math:`x >= y` elementwise, which is equivalent function to the overloaded operator `>=`.
  
      Args:
          x(Variable): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64. 
          y(Variable): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
          cond(Variable, optional): Optional output which can be any created Variable that meets the requirements to store the result of *greater_equal*.
              if cond is None, a new Varibale will be created to store the result.
          name(str, optional): The default value is None.  Normally there is no need for
              user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          Variable, the output data type is bool: The tensor variable storing the output, the output shape is same as input :attr:`x`.
  
      Examples:
          .. code-block:: python
  
            import paddle.fluid as fluid
            import numpy as np
  
            label = fluid.layers.assign(np.array([2, 2], dtype='int32'))
            limit = fluid.layers.assign(np.array([2, 3], dtype='int32'))
            out = fluid.layers.greater_equal(x=label, y=limit) #out=[True, False]
            out_1 = label >= limit #out1=[True, False]
  
      """
      check_variable_and_dtype(x, "x", ["float32", "float64", "int32", "int64"],
                               "greater_equal")
      check_variable_and_dtype(y, "y", ["float32", "float64", "int32", "int64"],
                               "greater_equal")
      if cond is not None:
          check_type(cond, "cond", Variable, "greater_equal")
  
      helper = LayerHelper("greater_equal", **locals())
      if cond is None:
          cond = helper.create_variable_for_type_inference(dtype='bool')
          cond.stop_gradient = True
  
      attrs = dict()
  
      helper.append_op(
          type='greater_equal',
          inputs={'X': [x],
                  'Y': [y]},
          outputs={'Out': [cond]},
          attrs=attrs)
      return cond
function_tokens: def greater_equal ( x , y , cond = None , name = None ) : check_variable_and_dtype ( x , "x" , [ "float32" , "float64" , "int32" , "int64" ] , "greater_equal" ) check_variable_and_dtype ( y , "y" , [ "float32" , "float64" , "int32" , "int64" ] , "greater_equal" ) if cond is not None : check_type ( cond , "cond" , Variable , "greater_equal" ) helper = LayerHelper ( "greater_equal" , * * locals ( ) ) if cond is None : cond = helper . create_variable_for_type_inference ( dtype = 'bool' ) cond . stop_gradient = True attrs = dict ( ) helper . append_op ( type = 'greater_equal' , inputs = { 'X' : [ x ] , 'Y' : [ y ] } , outputs = { 'Out' : [ cond ] } , attrs = attrs ) return cond