* Documentation: the generated OpenAPI (and the UI at `/docs`) will
                    show it as the response (JSON Schema).
                * Serialization: you could return an arbitrary object and the
                    `response_model` would be used to serialize that object into the
                    corresponding JSON.
                * Filtering: the JSON sent to the client will only contain the data

                * Documentation: the generated OpenAPI (and the UI at `/docs`) will
                    show it as the response (JSON Schema).
                * Serialization: you could return an arbitrary object and the
                    `response_model` would be used to serialize that object into the
                    corresponding JSON.
                * Filtering: the JSON sent to the client will only contain the data

<p>
The following terms are used to denote specific Unicode character classes:
</p>
<pre class="ebnf">
newline        = /* the Unicode code point U+000A */ .
unicode_char   = /* an arbitrary Unicode code point except newline */ .
unicode_letter = /* a Unicode code point classified as "Letter" */ .
unicode_digit  = /* a Unicode code point classified as "Number, decimal digit" */ .
</pre>

<p>

Object.keys(this.options.modifiers)\n      .map(name => ({\n        name,\n        ...this.options.modifiers[name],\n      }))\n      // sort the modifiers by order\n      .sort((a, b) => a.order - b.order);\n\n    // modifiers have the ability to execute arbitrary code when Popper.js get inited\n    // such code is executed in the same order of its modifier\n    // they could add new properties to their options configuration\n    // BE AWARE: don't add options to `options.modifiers.name` but to `modifierOptions`!\n...

        TF_TensorToTensorV1(c_inputs[i], &(*input_pairs)[i].second);
    if (!status->status.ok()) return false;
  }
  return true;
}

// Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
// result in a zero-sized tensor.
static TF_Tensor* EmptyTensor(TF_DataType dtype,
                              const tensorflow::TensorShape& shape) {
  static char empty;
  int64_t nelems = 1;