parameters: (x1, x2, dtype=None)
return_statement: return _apply_tensor_op(F.tensor_le, x1, x2, dtype=dtype)
docstring: Returns the truth value of ``(x1 <= x2)`` element-wise.
  
      Note:
          Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`,
          and `extobj` are not supported.
  
      Args:
          x1 (Tensor): Input array.
          x2 (Tensor): Input array. If ``x1.shape != x2.shape``, they must be
              broadcastable to a common shape (which becomes the shape of the output).
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
  
      Returns:
         Tensor or scalar, element-wise comparison of `x1` and `x2`. Typically of type
         bool, unless `dtype` is passed. This is a scalar if both `x1` and `x2` are
         scalars.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> output = np.less_equal(np.array([4, 2, 1]), np.array([2, 2, 2]))
          >>> print(output)
          [False  True  True]
docstring_summary: Returns the truth value of ``(x1 <= x2)`` element-wise.
docstring_tokens: Returns the truth value of ( x1 < = x2 ) element - wise .
function: def less_equal(x1, x2, dtype=None):
      """
      Returns the truth value of ``(x1 <= x2)`` element-wise.
  
      Note:
          Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`,
          and `extobj` are not supported.
  
      Args:
          x1 (Tensor): Input array.
          x2 (Tensor): Input array. If ``x1.shape != x2.shape``, they must be
              broadcastable to a common shape (which becomes the shape of the output).
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
  
      Returns:
         Tensor or scalar, element-wise comparison of `x1` and `x2`. Typically of type
         bool, unless `dtype` is passed. This is a scalar if both `x1` and `x2` are
         scalars.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> output = np.less_equal(np.array([4, 2, 1]), np.array([2, 2, 2]))
          >>> print(output)
          [False  True  True]
      """
      _check_input_tensor(x1, x2)
      return _apply_tensor_op(F.tensor_le, x1, x2, dtype=dtype)
function_tokens: def less_equal ( x1 , x2 , dtype = None ) : _check_input_tensor ( x1 , x2 ) return _apply_tensor_op ( F . tensor_le , x1 , x2 , dtype = dtype )