def _compute_sampled_logits(weights,
                            biases,
                            labels,
                            inputs,
                            num_sampled,
                            num_classes,
                            num_true=1,
                            sampled_values=None,
                            subtract_log_q=True,
                            remove_accidental_hits=False,
                            partition_strategy="mod",
                            name=None,
                            seed=None):

  if isinstance(weights, variables.PartitionedVariable):
    weights = list(weights)
  if not isinstance(weights, list):
    weights = [weights]

  with ops.name_scope(name, "compute_sampled_logits",
                      weights + [biases, inputs, labels]):
    if labels.dtype != dtypes.int64:
      labels = math_ops.cast(labels, dtypes.int64)
    labels_flat = array_ops.reshape(labels, [-1])

    # Sample the negative labels.
    #   sampled shape: [num_sampled] tensor
    #   true_expected_count shape = [batch_size, 1] tensor
    #   sampled_expected_count shape = [num_sampled] tensor
    if sampled_values is None:
      sampled_values = candidate_sampling_ops.log_uniform_candidate_sampler(
          true_classes=labels,
          num_true=num_true,
          num_sampled=num_sampled,
          unique=True,
          range_max=num_classes,
          seed=seed)
    # NOTE: pylint cannot tell that 'sampled_values' is a sequence
    # pylint: disable=unpacking-non-sequence
    sampled, true_expected_count, sampled_expected_count = (
        array_ops.stop_gradient(s) for s in sampled_values)
    # pylint: enable=unpacking-non-sequence
    sampled = math_ops.cast(sampled, dtypes.int64)

    # labels_flat is a [batch_size * num_true] tensor
    # sampled is a [num_sampled] int tensor
    all_ids = array_ops.concat([labels_flat, sampled], 0)

    # Retrieve the true weights and the logits of the sampled weights.

    # weights shape is [num_classes, dim]
    all_w = embedding_ops.embedding_lookup(
        weights, all_ids, partition_strategy=partition_strategy)
    if all_w.dtype != inputs.dtype:
      all_w = math_ops.cast(all_w, inputs.dtype)

    # true_w shape is [batch_size * num_true, dim]
    true_w = array_ops.slice(all_w, [0, 0],
                             array_ops.stack(
                                 [array_ops.shape(labels_flat)[0], -1]))

    sampled_w = array_ops.slice(
        all_w, array_ops.stack([array_ops.shape(labels_flat)[0], 0]), [-1, -1])
    # inputs has shape [batch_size, dim]
    # sampled_w has shape [num_sampled, dim]
    # Apply X*W', which yields [batch_size, num_sampled]
    sampled_logits = math_ops.matmul(inputs, sampled_w, transpose_b=True)

    # Retrieve the true and sampled biases, compute the true logits, and
    # add the biases to the true and sampled logits.
    all_b = embedding_ops.embedding_lookup(
        biases, all_ids, partition_strategy=partition_strategy)
    if all_b.dtype != inputs.dtype:
      all_b = math_ops.cast(all_b, inputs.dtype)
    # true_b is a [batch_size * num_true] tensor
    # sampled_b is a [num_sampled] float tensor
    true_b = array_ops.slice(all_b, [0], array_ops.shape(labels_flat))
    sampled_b = array_ops.slice(all_b, array_ops.shape(labels_flat), [-1])

    # inputs shape is [batch_size, dim]
    # true_w shape is [batch_size * num_true, dim]
    # row_wise_dots is [batch_size, num_true, dim]
    dim = array_ops.shape(true_w)[1:2]
    new_true_w_shape = array_ops.concat([[-1, num_true], dim], 0)
    row_wise_dots = math_ops.multiply(
        array_ops.expand_dims(inputs, 1),
        array_ops.reshape(true_w, new_true_w_shape))
    # We want the row-wise dot plus biases which yields a
    # [batch_size, num_true] tensor of true_logits.
    dots_as_matrix = array_ops.reshape(row_wise_dots,
                                       array_ops.concat([[-1], dim], 0))
    true_logits = array_ops.reshape(_sum_rows(dots_as_matrix), [-1, num_true])
    true_b = array_ops.reshape(true_b, [-1, num_true])
    true_logits += true_b
    sampled_logits += sampled_b

    if remove_accidental_hits:
      acc_hits = candidate_sampling_ops.compute_accidental_hits(
          labels, sampled, num_true=num_true)
      acc_indices, acc_ids, acc_weights = acc_hits

      # This is how SparseToDense expects the indices.
      acc_indices_2d = array_ops.reshape(acc_indices, [-1, 1])
      acc_ids_2d_int32 = array_ops.reshape(
          math_ops.cast(acc_ids, dtypes.int32), [-1, 1])
      sparse_indices = array_ops.concat([acc_indices_2d, acc_ids_2d_int32], 1,
                                        "sparse_indices")
      # Create sampled_logits_shape = [batch_size, num_sampled]
      sampled_logits_shape = array_ops.concat(
          [array_ops.shape(labels)[:1],
           array_ops.expand_dims(num_sampled, 0)], 0)
      if sampled_logits.dtype != acc_weights.dtype:
        acc_weights = math_ops.cast(acc_weights, sampled_logits.dtype)
      sampled_logits += gen_sparse_ops.sparse_to_dense(
          sparse_indices,
          sampled_logits_shape,
          acc_weights,
          default_value=0.0,
          validate_indices=False)

    if subtract_log_q:
      # Subtract log of Q(l), prior probability that l appears in sampled.
      true_logits -= math_ops.log(true_expected_count)
      sampled_logits -= math_ops.log(sampled_expected_count)

    # Construct output logits and labels. The true labels/logits start at col 0.
    out_logits = array_ops.concat([true_logits, sampled_logits], 1)

    # true_logits is a float tensor, ones_like(true_logits) is a float
    # tensor of ones. We then divide by num_true to ensure the per-example
    # labels sum to 1.0, i.e. form a proper probability distribution.
    out_labels = array_ops.concat([
        array_ops.ones_like(true_logits) / num_true,
        array_ops.zeros_like(sampled_logits)
    ], 1)

    return out_logits, out_labels