code: def dense_bincount(
      inputs, depth, binary_output, dtype, count_weights=None
  ):
      """Apply binary or count encoding to an input."""
      result = tf.math.bincount(
          inputs,
          weights=count_weights,
          minlength=depth,
          maxlength=depth,
          dtype=dtype,
          axis=-1,
          binary_output=binary_output,
      )
      if inputs.shape.rank == 1:
          result.set_shape(tf.TensorShape((depth,)))
      else:
          batch_size = inputs.shape.as_list()[0]
          result.set_shape(tf.TensorShape((batch_size, depth)))
      return result
  
imports: from keras.utils.module_utils import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: dense_bincount(inputs, depth, binary_output, dtype, count_weights) and returns: result