code: def _buckets(data, bucket_count=None):
      """Create a TensorFlow op to group data into histogram buckets.
  
      Arguments:
        data: A `Tensor` of any shape. Must be castable to `float64`.
        bucket_count: Optional positive `int` or scalar `int32` `Tensor`.
      Returns:
        A `Tensor` of shape `[k, 3]` and type `float64`. The `i`th row is
        a triple `[left_edge, right_edge, count]` for a single bucket.
        The value of `k` is either `bucket_count` or `1` or `0`.
      """
      # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
      import tensorflow.compat.v1 as tf
  
      if bucket_count is None:
          bucket_count = summary_v2.DEFAULT_BUCKET_COUNT
      with tf.name_scope(
          "buckets", values=[data, bucket_count]
      ), tf.control_dependencies(
          [
              tf.assert_scalar(bucket_count),
              tf.assert_type(bucket_count, tf.int32),
          ]
      ):
          data = tf.reshape(data, shape=[-1])  # flatten
          data = tf.cast(data, tf.float64)
          is_empty = tf.equal(tf.size(input=data), 0)
  
          def when_empty():
              return tf.constant([], shape=(0, 3), dtype=tf.float64)
  
          def when_nonempty():
              min_ = tf.reduce_min(input_tensor=data)
              max_ = tf.reduce_max(input_tensor=data)
              range_ = max_ - min_
              is_singular = tf.equal(range_, 0)
  
              def when_nonsingular():
                  bucket_width = range_ / tf.cast(
                      bucket_count, tf.float64
                  )
                  offsets = data - min_
                  bucket_indices = tf.cast(
                      tf.floor(offsets / bucket_width), dtype=tf.int32
                  )
                  clamped_indices = tf.minimum(
                      bucket_indices, bucket_count - 1
                  )
                  # Use float64 instead of float32 to avoid accumulating floating point error
                  # later in tf.reduce_sum when summing more than 2^24 individual `1.0` values.
                  # See https://github.com/tensorflow/tensorflow/issues/51419 for details.
                  one_hots = tf.one_hot(
                      clamped_indices,
                      depth=bucket_count,
                      dtype=tf.float64,
                  )
                  bucket_counts = tf.cast(
                      tf.reduce_sum(input_tensor=one_hots, axis=0),
                      dtype=tf.float64,
                  )
                  edges = tf.linspace(min_, max_, bucket_count + 1)
                  left_edges = edges[:-1]
                  right_edges = edges[1:]
                  return tf.transpose(
                      a=tf.stack(
                          [left_edges, right_edges, bucket_counts]
                      )
                  )
  
              def when_singular():
                  center = min_
                  bucket_starts = tf.stack([center - 0.5])
                  bucket_ends = tf.stack([center + 0.5])
                  bucket_counts = tf.stack(
                      [tf.cast(tf.size(input=data), tf.float64)]
                  )
                  return tf.transpose(
                      a=tf.stack(
                          [bucket_starts, bucket_ends, bucket_counts]
                      )
                  )
  
              return tf.cond(
                  is_singular, when_singular, when_nonsingular
              )
  
          return tf.cond(is_empty, when_empty, when_nonempty)
  
imports: import numpy
  from tensorboard.plugins.histogram import metadata
  from tensorboard.plugins.histogram import summary_v2
label_desc: Use image node_id 1 for calling a global function with example usage: _buckets(data, bucket_count) and returns: tf, tf, tf, tf, tf