} else { // part of header can be accommodated
			r.opts.HeaderSize -= b - 1
			need = 1 // to ensure we read at least one byte for every Read
			tokens = b
		}
	} else { // all tokens go towards payload
		need = int(math.Min(float64(b), float64(need)))
		tokens = need
	}
	// reduce tokens requested according to availability
	av := int(r.throttle.Tokens())
	if av < tokens && av > 0 {
		tokens = av

  chmod +x "/usr/local/bin/bazel"
fi

# "TFCI_MACOS_UPGRADE_PYENV_ENABLE" is used to decide if we need to upgrade the
# Pyenv version. We enable this for macOS x86 builds as the default Pyenv on
# those VMs does not support installing Python 3.12 and above which we need
# for running smoke tests in nightly/release wheel builds.
if [[ "${TFCI_MACOS_UPGRADE_PYENV_ENABLE}" == 1 ]]; then
  echo "Upgrading pyenv..."

  DefaultExpressionEvaluator used within. See the above discussion for more
  on why this evaluator is bad. We need to provide either an alternative
  implementation under a different roleHint, or else replace the
  BasicComponentConfigurator.

* Other

  We may need to define a new lifecycle/phase to contextualize a mojo right
  before it's used, and reset it's state afterward. Up to now, the approach

    # o schemaHtmlFileName: (NotRequired - Default 'schema-[project-name].html')
    #  The file name (not contain path) of SchemaHtml.
    #  Basically you don't need this.
    #  (For example, when you use Application Behavior, you need this)
    #
    #; schemaHtmlFileName = xxx.html
    # - - - - - - - - - -/

    # /- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Doing this provides the following specific benefits:
- **Reduce the API surface** - We no longer need to maintain two methods.
- **Consistency** - All languages have consistent access to the same APIs and ergonomics in the DSL.
- **Reduce the size of the Gradle distribution** - We no longer need to carry multiple standard libraries for different languages.

## Decision

  EXPECT_EQ(1, TF_OperationOutputListLength(feed, "output", s));
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(0, TF_OperationNumInputs(feed));
  EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
  EXPECT_EQ(0, TF_OperationNumControlInputs(feed));
  EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));

  tensorflow::AttrValue attr_value;

import org.apache.maven.plugin.descriptor.MojoDescriptor;

// TODO lifecycles being executed
// TODO what runs in each phase
// TODO plugins that need downloading
// TODO project dependencies that need downloading
// TODO unfortunately the plugins need to be downloaded in order to get the plugin.xml file. need to externalize this
//      from the plugin archive.
// TODO this will be the class that people get in IDEs to modify

/**

///

## Order the parameters as you need { #order-the-parameters-as-you-need }

/// tip

This is probably not as important or necessary if you use `Annotated`.

///

Let's say that you want to declare the query parameter `q` as a required `str`.

And you don't need to declare anything else for that parameter, so you don't really need to use `Query`.

## Caveat { #caveat }

If you need to perform heavy background computation and you don't necessarily need it to be run by the same process (for example, you don't need to share memory, variables, etc), you might benefit from using other bigger tools like <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a>.

    own the intellectual property, then you'll need to sign an [individual
    CLA][]. Please include your GitHub username.
  - If you work for a company that wants to allow you to contribute your work,
    then you'll need to sign a [corporate CLA][].

You generally only need to submit a CLA once, so if you've already submitted
one (even if it was for a different project), you probably don't need to do it
again.