from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check


class Arctanh(function_node.FunctionNode):

    """Elementwise inverse hyperbolic tangent function."""

    def check_type_forward(self, in_types):
        type_check._argname(in_types, ('x',))
        x_type, = in_types

        type_check.expect(x_type.dtype.kind == 'f')

    def forward(self, inputs):
        self.retain_inputs((0,))
        x, = inputs
        xp = cuda.get_array_module(x)
        y = xp.arctanh(x)
        return utils.force_array(y, dtype=x.dtype),

    def backward(self, indexes, grad_outputs):
        x, = self.get_retained_inputs()
        gy, = grad_outputs
        gx = 1. / (1 - x ** 2) * gy
        return gx,


def arctanh(x):
    """Elementwise inverse hyperbolic tangent function.

    Args:
        x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.

    Returns:
        ~chainer.Variable: Output variable.

    """
    return Arctanh().apply((x,))[0]

from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check


class Arctanh(function_node.FunctionNode):

    """Elementwise inverse hyperbolic tangent function."""

    def check_type_forward(self, in_types):
        if hasattr(type_check, '_argname'):
            # typecheck._argname is introduced by Chainer v6
            type_check._argname(in_types, ('x',))
        x_type, = in_types

        type_check.expect(x_type.dtype.kind == 'f')

    def forward(self, inputs):
        self.retain_inputs((0,))
        x, = inputs
        xp = cuda.get_array_module(x)
        y = xp.arctanh(x)
        return utils.force_array(y, dtype=x.dtype),

    def backward(self, indexes, grad_outputs):
        x, = self.get_retained_inputs()
        gy, = grad_outputs
        gx = 1. / (1 - x ** 2) * gy
        return gx,


def arctanh(x):
    """Elementwise inverse hyperbolic tangent function.

    Args:
        x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.

    Returns:
        ~chainer.Variable: Output variable.

    """
    return Arctanh().apply((x,))[0]

Fix chainer v4 error about type_check._argname
Fix chainer v4 error about type_check._argname

  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import type_check
  
  
  class Arctanh(function_node.FunctionNode):
  
      """Elementwise inverse hyperbolic tangent function."""
  
      def check_type_forward(self, in_types):
+         if hasattr(type_check, '_argname'):
+             # typecheck._argname is introduced by Chainer v6
-         type_check._argname(in_types, ('x',))
+             type_check._argname(in_types, ('x',))
          x_type, = in_types
  
          type_check.expect(x_type.dtype.kind == 'f')
  
      def forward(self, inputs):
          self.retain_inputs((0,))
          x, = inputs
          xp = cuda.get_array_module(x)
          y = xp.arctanh(x)
          return utils.force_array(y, dtype=x.dtype),
  
      def backward(self, indexes, grad_outputs):
          x, = self.get_retained_inputs()
          gy, = grad_outputs
          gx = 1. / (1 - x ** 2) * gy
          return gx,
  
  
  def arctanh(x):
      """Elementwise inverse hyperbolic tangent function.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
  
      Returns:
          ~chainer.Variable: Output variable.
  
      """
      return Arctanh().apply((x,))[0]
  
Fix chainer v4 error about type_check._argname
## Code Before:
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check


class Arctanh(function_node.FunctionNode):

    """Elementwise inverse hyperbolic tangent function."""

    def check_type_forward(self, in_types):
        type_check._argname(in_types, ('x',))
        x_type, = in_types

        type_check.expect(x_type.dtype.kind == 'f')

    def forward(self, inputs):
        self.retain_inputs((0,))
        x, = inputs
        xp = cuda.get_array_module(x)
        y = xp.arctanh(x)
        return utils.force_array(y, dtype=x.dtype),

    def backward(self, indexes, grad_outputs):
        x, = self.get_retained_inputs()
        gy, = grad_outputs
        gx = 1. / (1 - x ** 2) * gy
        return gx,


def arctanh(x):
    """Elementwise inverse hyperbolic tangent function.

    Args:
        x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.

    Returns:
        ~chainer.Variable: Output variable.

    """
    return Arctanh().apply((x,))[0]

## Instruction:
Fix chainer v4 error about type_check._argname
## Code After:
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check


class Arctanh(function_node.FunctionNode):

    """Elementwise inverse hyperbolic tangent function."""

    def check_type_forward(self, in_types):
        if hasattr(type_check, '_argname'):
            # typecheck._argname is introduced by Chainer v6
            type_check._argname(in_types, ('x',))
        x_type, = in_types

        type_check.expect(x_type.dtype.kind == 'f')

    def forward(self, inputs):
        self.retain_inputs((0,))
        x, = inputs
        xp = cuda.get_array_module(x)
        y = xp.arctanh(x)
        return utils.force_array(y, dtype=x.dtype),

    def backward(self, indexes, grad_outputs):
        x, = self.get_retained_inputs()
        gy, = grad_outputs
        gx = 1. / (1 - x ** 2) * gy
        return gx,


def arctanh(x):
    """Elementwise inverse hyperbolic tangent function.

    Args:
        x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.

    Returns:
        ~chainer.Variable: Output variable.

    """
    return Arctanh().apply((x,))[0]
