code: def string_format(
      template, inputs, placeholder="{}", summarize=3, name=None
  ):
      r"""Formats a string template using a list of tensors.
  
      Formats a string template using a list of tensors, abbreviating tensors by
      only printing the first and last `summarize` elements of each dimension
      (recursively). If formatting only one tensor into a template, the tensor does
      not have to be wrapped in a list.
  
      Example:
        Formatting a single-tensor template:
  
        >>> tensor = tf.range(5)
        >>> tf.strings.format("tensor: {}, suffix", tensor)
        <tf.Tensor: shape=(), dtype=string, numpy=b'tensor: [0 1 2 3 4], suffix'>
  
        Formatting a multi-tensor template:
  
        >>> tensor_a = tf.range(2)
        >>> tensor_b = tf.range(1, 4, 2)
        >>> tf.strings.format("a: {}, b: {}, suffix", (tensor_a, tensor_b))
        <tf.Tensor: shape=(), dtype=string, numpy=b'a: [0 1], b: [1 3], suffix'>
  
  
      Args:
        template: A string template to format tensor values into.
        inputs: A list of `Tensor` objects, or a single Tensor.
          The list of tensors to format into the template string. If a solitary
          tensor is passed in, the input tensor will automatically be wrapped as a
          list.
        placeholder: An optional `string`. Defaults to `{}`.
          At each placeholder occurring in the template, a subsequent tensor
          will be inserted.
        summarize: An optional `int`. Defaults to `3`.
          When formatting the tensors, show the first and last `summarize`
          entries of each tensor dimension (recursively). If set to -1, all
          elements of the tensor will be shown.
        name: A name for the operation (optional).
  
      Returns:
        A scalar `Tensor` of type `string`.
  
      Raises:
        ValueError: if the number of placeholders does not match the number of
          inputs.
      """
      # If there is only one tensor to format, we will automatically wrap it in a
      # list to simplify the user experience
      if tensor_util.is_tf_type(inputs):
          inputs = [inputs]
      if template.count(placeholder) != len(inputs):
          raise ValueError(
              f"The template expects {template.count(placeholder)} "
              f"tensors, but the inputs only has {len(inputs)}. "
              "Please ensure the number of placeholders in template "
              "matches inputs length."
          )
  
      return gen_string_ops.string_format(
          inputs,
          template=template,
          placeholder=placeholder,
          summarize=summarize,
          name=name,
      )
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_parsing_ops
  from tensorflow.python.ops import gen_string_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.gen_string_ops import *
  from tensorflow.python.util import compat
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 3 for calling a global function with example usage: string_format(template, inputs, placeholder, summarize, name) and returns: gen_string_ops