class_docstr: Determines which tensors will be ready when running the graph.
  
  Determines which tensors from `fetches` are ready to run, using following
  algorithm.
  
  1. Determine which table initializers are ready to run.  A table initializer
     is an element of the TABLE_INITIALIZERS collection and it is ready to run
     if all the tensors it depends on are set to ready in
     `replaced_tensors_ready`.
  
  2. Determine which of `fetches` are ready to run.  A fetch is ready to run if
     it only depends on tensors in `feeds` and tensors that are set to ready in
     `replaced_tensors_ready`.
docstr: Gets the inputs that the given `tensor_or_op` transitively depends on.
  
  Args:
    tensor_or_op: A `Tensor`, `SparseTensor`, `RaggedTensor` or `Operation`.
  
  Returns:
    A dict of name to `Tensor`, `SparseTensor`, or `RaggedTensor` (sub-dict of
    `input_signature`) that the given `tensor_or_op` depends on.
  
  Raises:
    TypeError: If `tensor_or_op` is of an unsupported type.
docstr_tok: ["Gets","the","inputs","that","the","given","`","tensor_or_op","`","transitively","depends","on",".","Args",":","tensor_or_op",":","A","`","Tensor","`",",","`","SparseTensor","`",",","`","RaggedTensor","`","or","`","Operation","`",".","Returns",":","A","dict","of","name","to","`","Tensor","`",",","`","SparseTensor","`",",","or","`","RaggedTensor","`","(","sub-dict","of","`","input_signature","`",")","that","the","given","`","tensor_or_op","`","depends","on",".","Raises",":","TypeError",":","If","`","tensor_or_op","`","is","of","an","unsupported","type","."]
code: def get_dependent_inputs(self, tensor_or_op):
      """Gets the inputs that the given `tensor_or_op` transitively depends on.
  
      Args:
        tensor_or_op: A `Tensor`, `SparseTensor`, `RaggedTensor` or `Operation`.
  
      Returns:
        A dict of name to `Tensor`, `SparseTensor`, or `RaggedTensor` (sub-dict of
        `input_signature`) that the given `tensor_or_op` depends on.
  
      Raises:
        TypeError: If `tensor_or_op` is of an unsupported type.
      """
      if not isinstance(
          tensor_or_op,
          (tf.Tensor, tf.SparseTensor, tf.RaggedTensor, tf.Operation),
      ):
          raise TypeError(
              "Expected Tensor, SparseTensor, RaggedTensor or Operation got {} of "
              "type {}".format(tensor_or_op, type(tensor_or_op))
          )
  
      dependents = set()
      for component in _decompose_tensor_or_op(tensor_or_op):
          dependents.update(
              self._graph_analyzer.analyze_tensor(
                  component
              ).dependent_sources
          )
  
      result = {}
      for name, tensor in self._input_signature.items():
          if any(
              tf_utils.hashable_tensor_or_op(component) in dependents
              for component in _decompose_tensor_or_op(tensor)
          ):
              result[name] = tensor
      return result
  
imports: import collections
  import copy
  import hashlib
  import itertools
  from typing import Iterable, List, Mapping, Optional, Set, Union
  import uuid
  from absl import logging
  import tensorflow
  from tensorflow_transform import analyzer_nodes
  from tensorflow_transform import common_types
  from tensorflow_transform import nodes
  from tensorflow_transform import tf_utils
  from tfx_bsl.types import tfx_namedtuple
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import func_graph
  from tensorflow.python.framework import function_def_to_graph
  from tensorflow.python.util import object_identity
label_desc: Use image node_id 9 for calling the InitializableGraphAnalyzer obj's underlying member method code with example usage: obj.get_dependent_inputs(tensor_or_op) and returns: result