code: def _identity_jvp(attr_tuple, inputs, outputs, tangents):
      # Special-cased mostly for resource handles, where creating ones Tensors from
      # handle data for transposing the backward function on the tape is error-prone
      # (even if we get good handle data, partially defined shapes are an issue).
      del attr_tuple, inputs, outputs
      return [array_ops.identity(t) for t in tangents]
  
imports: import functools
  import threading
  from tensorflow.core.function.polymorphism import function_cache
  from tensorflow.python import pywrap_tfe
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import backprop_util
  from tensorflow.python.eager import execute
  from tensorflow.python.eager import forwardprop_util
  from tensorflow.python.eager.polymorphic_function import tracing_compilation
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops.parallel_for import control_flow_ops
  from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: _identity_jvp(attr_tuple, inputs, outputs, tangents) and returns: unknown