parameters: (x1, x2, dtype=None)
return_statement: return _apply_tensor_op(F.tensor_mul, x1, x2, dtype=dtype)
docstring: Multiplies arguments element-wise.
  
      Note:
          Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
          not supported.
  
      Args:
          x1 (Tensor): input tensor to be multiplied.
          x2 (Tensor): input tensor to be multiplied.
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
  
      Returns:
          Tensor or scalar, the product of `x1` and `x2`, element-wise. This is a scalar
          if both `x1` and `x2` are scalars.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> x1 = np.full((3, 2), [1, 2])
          >>> x2 = np.full((3, 2), [3, 4])
          >>> output = np.multiply(x1, x2)
          >>> print(output)
          [[3 8]
          [3 8]
          [3 8]]
docstring_summary: Multiplies arguments element-wise.
docstring_tokens: Multiplies arguments element - wise .
function: def multiply(x1, x2, dtype=None):
      """
      Multiplies arguments element-wise.
  
      Note:
          Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
          not supported.
  
      Args:
          x1 (Tensor): input tensor to be multiplied.
          x2 (Tensor): input tensor to be multiplied.
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
  
      Returns:
          Tensor or scalar, the product of `x1` and `x2`, element-wise. This is a scalar
          if both `x1` and `x2` are scalars.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> x1 = np.full((3, 2), [1, 2])
          >>> x2 = np.full((3, 2), [3, 4])
          >>> output = np.multiply(x1, x2)
          >>> print(output)
          [[3 8]
          [3 8]
          [3 8]]
      """
      if _get_device() == 'CPU':
          _check_input_tensor(x1, x2)
          # broadcast is not fully supported on CPU backend,
          # and explicit broadcasting is performed
          shape_out = _infer_out_shape(F.shape(x1), F.shape(x2))
          x1 = _broadcast_to_shape(x1, shape_out)
          x2 = _broadcast_to_shape(x2, shape_out)
      return _apply_tensor_op(F.tensor_mul, x1, x2, dtype=dtype)
function_tokens: def multiply ( x1 , x2 , dtype = None ) : if _get_device ( ) == 'CPU' : _check_input_tensor ( x1 , x2 ) # broadcast is not fully supported on CPU backend, # and explicit broadcasting is performed shape_out = _infer_out_shape ( F . shape ( x1 ) , F . shape ( x2 ) ) x1 = _broadcast_to_shape ( x1 , shape_out ) x2 = _broadcast_to_shape ( x2 , shape_out ) return _apply_tensor_op ( F . tensor_mul , x1 , x2 , dtype = dtype )