@tf_export('math.log_sigmoid', v1=['math.log_sigmoid', 'log_sigmoid'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('log_sigmoid')
def log_sigmoid(x, name=None):
    'Computes log sigmoid of `x` element-wise.\n\n  Specifically, `y = log(1 / (1 + exp(-x)))`.  For numerical stability,\n  we use `y = -tf.nn.softplus(-x)`.\n\n  Args:\n    x: A Tensor with type `float32` or `float64`.\n    name: A name for the operation (optional).\n\n  Returns:\n    A Tensor with the same type as `x`.\n  '
    with ops.name_scope(name, 'LogSigmoid', [x]) as name:
        x = ops.convert_to_tensor(x, name='x')
        return gen_math_ops.neg(gen_nn_ops.softplus((- x)), name=name)
Computes log sigmoid of `x` element-wise.

Specifically, `y = log(1 / (1 + exp(-x)))`.  For numerical stability,
we use `y = -tf.nn.softplus(-x)`.

Args:
  x: A Tensor with type `float32` or `float64`.
  name: A name for the operation (optional).

Returns:
  A Tensor with the same type as `x`.
@tf_export('math.log_sigmoid', v1=['math.log_sigmoid', 'log_sigmoid'])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints('log_sigmoid')
def log_sigmoid(x, name=None):
    'Computes log sigmoid of `x` element-wise.\n\n  Specifically, `y = log(1 / (1 + exp(-x)))`.  For numerical stability,\n  we use `y = -tf.nn.softplus(-x)`.\n\n  Args:\n    x: A Tensor with type `float32` or `float64`.\n    name: A name for the operation (optional).\n\n  Returns:\n    A Tensor with the same type as `x`.\n  '
    with ops.name_scope(name, 'LogSigmoid', [x]) as name:
        x = ops.convert_to_tensor(x, name='x')
        return gen_math_ops.neg(gen_nn_ops.softplus((- x)), name=name)