easy_rec/python/core/easyrec_metrics/distribute_metrics_impl_pai.py [2894:2986]:
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    update_op = compute_sensitivity_at_specificity(update_ops['tp'],
                                                   update_ops['tn'],
                                                   update_ops['fp'],
                                                   update_ops['fn'],
                                                   'update_op')
    if updates_collections:
      ops.add_to_collections(updates_collections, update_op)

    return sensitivity, update_op


def _expand_and_tile(tensor, multiple, dim=0, name=None):
  """Slice `tensor` shape in 2, then tile along the sliced dimension.

  A new dimension is inserted in shape of `tensor` before `dim`, then values are
  tiled `multiple` times along the new dimension.

  Args:
    tensor: Input `Tensor` or `SparseTensor`.
    multiple: Integer, number of times to tile.
    dim: Integer, dimension along which to tile.
    name: Name of operation.

  Returns:
    `Tensor` result of expanding and tiling `tensor`.

  Raises:
    ValueError: if `multiple` is less than 1, or `dim` is not in
    `[-rank(tensor), rank(tensor)]`.
  """
  if multiple < 1:
    raise ValueError('Invalid multiple %s, must be > 0.' % multiple)
  with ops.name_scope(name, 'expand_and_tile',
                      (tensor, multiple, dim)) as scope:
    # Sparse.
    tensor = sparse_tensor.convert_to_tensor_or_sparse_tensor(tensor)
    if isinstance(tensor, sparse_tensor.SparseTensor):
      if dim < 0:
        expand_dims = array_ops.reshape(
            array_ops.size(tensor.dense_shape) + dim, [1])
      else:
        expand_dims = [dim]
      expanded_shape = array_ops.concat(
          (array_ops.slice(tensor.dense_shape, [0], expand_dims), [1],
           array_ops.slice(tensor.dense_shape, expand_dims, [-1])),
          0,
          name='expanded_shape')
      expanded = sparse_ops.sparse_reshape(
          tensor, shape=expanded_shape, name='expand')
      if multiple == 1:
        return expanded
      return sparse_ops.sparse_concat(
          dim - 1 if dim < 0 else dim, [expanded] * multiple, name=scope)

    # Dense.
    expanded = array_ops.expand_dims(
        tensor, dim if (dim >= 0) else (dim - 1), name='expand')
    if multiple == 1:
      return expanded
    ones = array_ops.ones_like(array_ops.shape(tensor))
    tile_multiples = array_ops.concat((ones[:dim], (multiple,), ones[dim:]),
                                      0,
                                      name='multiples')
    return array_ops.tile(expanded, tile_multiples, name=scope)


def _num_relevant(labels, k):
  """Computes number of relevant values for each row in labels.

  For labels with shape [D1, ... DN, num_labels], this is the minimum of
  `num_labels` and `k`.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels].
    k: Integer, k for @k metric.

  Returns:
    Integer `Tensor` of shape [D1, ... DN], where each value is the number of
    relevant values for that row.

  Raises:
    ValueError: if inputs have invalid dtypes or values.
  """
  if k < 1:
    raise ValueError('Invalid k=%s.' % k)
  with ops.name_scope(None, 'num_relevant', (labels,)) as scope:
    # For SparseTensor, calculate separate count for each row.
    labels = sparse_tensor.convert_to_tensor_or_sparse_tensor(labels)
    if isinstance(labels, sparse_tensor.SparseTensor):
      return math_ops.minimum(sets.set_size(labels), k, name=scope)
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easy_rec/python/core/easyrec_metrics/distribute_metrics_impl_tf.py [2908:3000]:
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    update_op = compute_sensitivity_at_specificity(update_ops['tp'],
                                                   update_ops['tn'],
                                                   update_ops['fp'],
                                                   update_ops['fn'],
                                                   'update_op')
    if updates_collections:
      ops.add_to_collections(updates_collections, update_op)

    return sensitivity, update_op


def _expand_and_tile(tensor, multiple, dim=0, name=None):
  """Slice `tensor` shape in 2, then tile along the sliced dimension.

  A new dimension is inserted in shape of `tensor` before `dim`, then values are
  tiled `multiple` times along the new dimension.

  Args:
    tensor: Input `Tensor` or `SparseTensor`.
    multiple: Integer, number of times to tile.
    dim: Integer, dimension along which to tile.
    name: Name of operation.

  Returns:
    `Tensor` result of expanding and tiling `tensor`.

  Raises:
    ValueError: if `multiple` is less than 1, or `dim` is not in
    `[-rank(tensor), rank(tensor)]`.
  """
  if multiple < 1:
    raise ValueError('Invalid multiple %s, must be > 0.' % multiple)
  with ops.name_scope(name, 'expand_and_tile',
                      (tensor, multiple, dim)) as scope:
    # Sparse.
    tensor = sparse_tensor.convert_to_tensor_or_sparse_tensor(tensor)
    if isinstance(tensor, sparse_tensor.SparseTensor):
      if dim < 0:
        expand_dims = array_ops.reshape(
            array_ops.size(tensor.dense_shape) + dim, [1])
      else:
        expand_dims = [dim]
      expanded_shape = array_ops.concat(
          (array_ops.slice(tensor.dense_shape, [0], expand_dims), [1],
           array_ops.slice(tensor.dense_shape, expand_dims, [-1])),
          0,
          name='expanded_shape')
      expanded = sparse_ops.sparse_reshape(
          tensor, shape=expanded_shape, name='expand')
      if multiple == 1:
        return expanded
      return sparse_ops.sparse_concat(
          dim - 1 if dim < 0 else dim, [expanded] * multiple, name=scope)

    # Dense.
    expanded = array_ops.expand_dims(
        tensor, dim if (dim >= 0) else (dim - 1), name='expand')
    if multiple == 1:
      return expanded
    ones = array_ops.ones_like(array_ops.shape(tensor))
    tile_multiples = array_ops.concat((ones[:dim], (multiple,), ones[dim:]),
                                      0,
                                      name='multiples')
    return array_ops.tile(expanded, tile_multiples, name=scope)


def _num_relevant(labels, k):
  """Computes number of relevant values for each row in labels.

  For labels with shape [D1, ... DN, num_labels], this is the minimum of
  `num_labels` and `k`.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels].
    k: Integer, k for @k metric.

  Returns:
    Integer `Tensor` of shape [D1, ... DN], where each value is the number of
    relevant values for that row.

  Raises:
    ValueError: if inputs have invalid dtypes or values.
  """
  if k < 1:
    raise ValueError('Invalid k=%s.' % k)
  with ops.name_scope(None, 'num_relevant', (labels,)) as scope:
    # For SparseTensor, calculate separate count for each row.
    labels = sparse_tensor.convert_to_tensor_or_sparse_tensor(labels)
    if isinstance(labels, sparse_tensor.SparseTensor):
      return math_ops.minimum(sets.set_size(labels), k, name=scope)
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