code: def slice(inputs, start_indices, shape):
      """Return a slice of an input tensor.
  
      At a high level, this operation is an explicit replacement for array slicing
      e.g. `inputs[start_indices: start_indices + shape]`.
      Unlike slicing via brackets, this operation will accept tensor start
      indices on all backends, which is useful when indices dynamically computed
      via other tensor operations.
  
      ```python
      inputs = np.zeros((5, 5))
      start_indices = np.array([3, 3])
      shape = np.array([2, 2])
      inputs = keras.ops.slice(inputs, start_indices, updates)
      ```
  
      Args:
          inputs: A tensor, the tensor to be updated.
          start_indices: A list/tuple of shape `(inputs.ndim,)`, specifying
              the starting indices for updating.
          shape: The full shape of the returned slice.
  
      Returns:
          A tensor, has the same shape and dtype as `inputs`.
      """
      if any_symbolic_tensors((inputs, start_indices, shape)):
          return Slice().symbolic_call(inputs, start_indices, shape)
      return backend.core.slice(inputs, start_indices, shape)
  
imports: import numpy
  from keras import backend
  from keras.api_export import keras_export
  from keras.backend import KerasTensor
  from keras.backend import any_symbolic_tensors
  from keras.ops.operation import Operation
  from keras.utils import traceback_utils
label_desc: Use image node_id 3 for calling a global function with example usage: slice(inputs, start_indices, shape) and returns: backend, Slice