old_contents: import chainer
  from chainer.backends import cuda
  from chainer import function_node
  import chainer.functions
  from chainer.functions.math import matmul
  from chainer import utils
  from chainer.utils import precision
  from chainer.utils import type_check
  
  
  class BatchDet(function_node.FunctionNode):
  
      @property
      def label(self):
          return 'det'
  
      def check_type_forward(self, in_types):
          type_check.argname(in_types, ('x',))
          a_type, = in_types
          type_check.expect(a_type.dtype.kind == 'f')
          # Only a minibatch of 2D array shapes allowed.
          type_check.expect(a_type.ndim == 3)
          # Matrix inversion only allowed for square matrices
          # so assert the last two dimensions are equal.
          type_check.expect(a_type.shape[-1] == a_type.shape[-2])
  
      @precision._fp16_mixed_precision_helper
      def forward(self, inputs):
          self.retain_inputs((0,))
          self.retain_outputs((0,))
          x, = inputs
          xp = cuda.get_array_module(x)
          detx = xp.linalg.det(x)
          return detx,
  
      def backward(self, indexes, gy):
          x, = self.get_retained_inputs()
          detx, = self.get_retained_outputs()
          gy, = gy
          inv_x = chainer.functions.batch_inv(
              chainer.functions.transpose(x, (0, 2, 1)))
          gy = chainer.functions.broadcast_to(gy[:, None, None], inv_x.shape)
          detx = chainer.functions.broadcast_to(detx[:, None, None], inv_x.shape)
          grad = gy * detx * inv_x
          return grad,
  
  
  def batch_det(a):
      """Computes the determinant of a batch of square matrices.
  
      Args:
          a (Variable): Input array to compute the determinant for.
          The first dimension should iterate over each matrix and be
          of the batchsize.
  
      Returns:
          ~chainer.Variable: vector of determinants for every matrix
          in the batch.
  
      """
      return BatchDet().apply((a,))[0]
  
  
  def det(a):
      """Computes the determinant of a single square matrix.
  
      Args:
          a (Variable): Input array to compute the determinant for.
  
      Returns:
          ~chainer.Variable: Scalar determinant of the matrix a.
  
      """
      shape = (1, a.shape[0], a.shape[1])
      batched_a = chainer.functions.reshape(a, shape)
      batched_det = BatchDet().apply((batched_a,))[0]
      return chainer.functions.reshape(batched_det, ())
  
new_contents: import chainer
  from chainer.backends import cuda
  from chainer import function_node
  import chainer.functions
  from chainer.utils import precision
  from chainer.utils import type_check
  
  
  class BatchDet(function_node.FunctionNode):
  
      @property
      def label(self):
          return 'det'
  
      def check_type_forward(self, in_types):
          type_check.argname(in_types, ('x',))
          a_type, = in_types
          type_check.expect(a_type.dtype.kind == 'f')
          # Only a minibatch of 2D array shapes allowed.
          type_check.expect(a_type.ndim == 3)
          # Matrix inversion only allowed for square matrices
          # so assert the last two dimensions are equal.
          type_check.expect(a_type.shape[-1] == a_type.shape[-2])
  
      @precision._fp16_mixed_precision_helper
      def forward(self, inputs):
          self.retain_inputs((0,))
          self.retain_outputs((0,))
          x, = inputs
          xp = cuda.get_array_module(x)
          detx = xp.linalg.det(x)
          return detx,
  
      def backward(self, indexes, gy):
          x, = self.get_retained_inputs()
          detx, = self.get_retained_outputs()
          gy, = gy
          inv_x = chainer.functions.batch_inv(
              chainer.functions.transpose(x, (0, 2, 1)))
          gy = chainer.functions.broadcast_to(gy[:, None, None], inv_x.shape)
          detx = chainer.functions.broadcast_to(detx[:, None, None], inv_x.shape)
          grad = gy * detx * inv_x
          return grad,
  
  
  def batch_det(a):
      """Computes the determinant of a batch of square matrices.
  
      Args:
          a (Variable): Input array to compute the determinant for.
          The first dimension should iterate over each matrix and be
          of the batchsize.
  
      Returns:
          ~chainer.Variable: vector of determinants for every matrix
          in the batch.
  
      """
      return BatchDet().apply((a,))[0]
  
  
  def det(a):
      """Computes the determinant of a single square matrix.
  
      Args:
          a (Variable): Input array to compute the determinant for.
  
      Returns:
          ~chainer.Variable: Scalar determinant of the matrix a.
  
      """
      shape = (1, a.shape[0], a.shape[1])
      batched_a = chainer.functions.reshape(a, shape)
      batched_det = BatchDet().apply((batched_a,))[0]
      return chainer.functions.reshape(batched_det, ())
  
subject: Remove unused imports