lengths_vector.push_back(v.size());
        }
        TFE_OpSetAttrStringList(op, attr_name, values_vector.data(),
                                lengths_vector.data(), s_size);
      }

      // Int
      if (const int i_size = default_value.list().i_size()) {
        absl::InlinedVector<int64_t, 4> i_vector;
        i_vector.reserve(i_size);
        for (int i = 0; i < i_size; ++i) {

		v, ok := tagsMap[k]
		if !ok || v != cv {
			mismatch = true
			break
		}
	}
	return !mismatch
}

// BySize returns true if sz satisfies one of ObjectSizeGreaterThan,
// ObjectSizeLessThan predicates or a combination of them via And.
func (f Filter) BySize(sz int64) bool {
	if f.ObjectSizeGreaterThan > 0 &&
		sz <= f.ObjectSizeGreaterThan {
		return false
	}
	if f.ObjectSizeLessThan > 0 &&

  DCHECK(tf_results->missing_unused_key_names_data.empty());

  size_t size = results.missing_unused_input_map_keys.size();
  tf_results->missing_unused_key_names.resize(size);
  tf_results->missing_unused_key_indexes.resize(size);

  for (int i = 0; i < size; ++i) {
    TensorId id = results.missing_unused_input_map_keys[i];
    tf_results->missing_unused_key_names_data.emplace_back(id.first);
    tf_results->missing_unused_key_names[i] =

  for (int device_index = 0; device_index < underlying_devices_.size();
       ++device_index) {
    DeviceThread* device_thread = device_threads_[device_index].get();
    std::vector<TFE_TensorHandle*> device_inputs;
    device_inputs.reserve(inputs.size());
    for (int input_index = 0; input_index < inputs.size(); ++input_index) {
      // Parallel tensors are divided between operations by device.

else if (unit === 'vh' || unit === 'vw') {\n    // if is a vh or vw, we calculate the size based on the viewport\n    let size;\n    if (unit === 'vh') {\n      size = Math.max(\n        document.documentElement.clientHeight,\n        window.innerHeight || 0\n      );\n    } else {\n      size = Math.max(\n        document.documentElement.clientWidth,\n        window.innerWidth || 0\n      );\n    }\n    return size / 100 * value;\n  } else {\n    // if is an explicit pixel unit, we get rid of the unit...

The `TestClient` does some magic inside to call the asynchronous FastAPI application in your normal `def` test functions, using standard pytest. But that magic doesn't work anymore when we're using it inside asynchronous functions. By running our tests asynchronously, we can no longer use the `TestClient` inside our test functions.

   * @since 22.0
   */
  public long approximateElementCount() {
    long bitSize = bits.bitSize();
    long bitCount = bits.bitCount();

    /**
     * Each insertion is expected to reduce the # of clear bits by a factor of
     * `numHashFunctions/bitSize`. So, after n insertions, expected bitCount is `bitSize * (1 - (1 -
     * numHashFunctions/bitSize)^n)`. Solving that for n, and approximating `ln x` as `x - 1` when x

* Use the `templates` you created to render and return a `TemplateResponse`, pass the name of the template, the request object, and a "context" dictionary with key-value pairs to be used inside of the Jinja2 template.

```Python hl_lines="4  11  15-18"
{!../../docs_src/templates/tutorial001.py!}
```

/// note

Before FastAPI 0.108.0, Starlette 0.29.0, the `name` was the first parameter.

///

If you used to rely on this behavior, now you should create the resources for background tasks inside the background task itself, and use internally only data that doesn't depend on the resources of dependencies with `yield`.

	for za0005 := range z.PartETags {
		s += msgp.StringPrefixSize + len(z.PartETags[za0005])