parameters: (op, received_grad)
return_statement: return (received_grad, gen_nn_ops.bias_add_grad(out_backprop=received_grad,
                                                    data_format=data_format))
docstring: Return the gradients for the 2 inputs of bias_op.
  
    The first input of unused_bias_op is the tensor t, and its gradient is
    just the gradient the unused_bias_op received.
  
    The second input of unused_bias_op is the bias vector which has one fewer
    dimension than "received_grad" (the batch dimension.)  Its gradient is the
    received gradient Summed on the batch dimension, which is the first dimension.
  
    Args:
      op: The BiasOp for which we need to generate gradients.
      received_grad: Tensor.  The gradients passed to the BiasOp.
  
    Returns:
      Two tensors, the first one for the "tensor" input of the BiasOp,
      the second one for the "bias" input of the BiasOp.
docstring_summary: Return the gradients for the 2 inputs of bias_op.
docstring_tokens: Return the gradients for the 2 inputs of bias_op .
function: def _BiasAddGrad(op, received_grad):
    """Return the gradients for the 2 inputs of bias_op.
  
    The first input of unused_bias_op is the tensor t, and its gradient is
    just the gradient the unused_bias_op received.
  
    The second input of unused_bias_op is the bias vector which has one fewer
    dimension than "received_grad" (the batch dimension.)  Its gradient is the
    received gradient Summed on the batch dimension, which is the first dimension.
  
    Args:
      op: The BiasOp for which we need to generate gradients.
      received_grad: Tensor.  The gradients passed to the BiasOp.
  
    Returns:
      Two tensors, the first one for the "tensor" input of the BiasOp,
      the second one for the "bias" input of the BiasOp.
    """
    try:
      data_format = op.get_attr("data_format")
    except ValueError:
      data_format = None
    return (received_grad, gen_nn_ops.bias_add_grad(out_backprop=received_grad,
                                                    data_format=data_format))
function_tokens: def _BiasAddGrad ( op , received_grad ) : try : data_format = op . get_attr ( "data_format" ) except ValueError : data_format = None return ( received_grad , gen_nn_ops . bias_add_grad ( out_backprop = received_grad , data_format = data_format ) )