* چگونگی تنظیم **<abbr title="Validation Constraints">محدودیت‌های اعتبارسنجی</abbr>** به عنوان مثال `maximum_length` یا `regex`.
* سیستم **<abbr title="also known as components, resources, providers, services, injectables">Dependency Injection</abbr>** قوی و کاربردی.
* امنیت و تایید هویت, شامل پشتیبانی از **OAuth2** مبتنی بر **JWT tokens** و **HTTP Basic**.
* تکنیک پیشرفته برای تعریف **مدل‌های چند سطحی JSON** (بر اساس Pydantic).

# Config to use a mostly-static build and disable modular op registration
# support (this will revert to loading TensorFlow with RTLD_GLOBAL in Python).
# By default, TensorFlow will build with a dependence on
# //tensorflow:libtensorflow_framework.so.
build:monolithic --define framework_shared_object=false
build:monolithic --define tsl_protobuf_header_only=false

Você também pode ter **outros motivos** que tornariam mais fácil ter um **único contêiner** com **múltiplos processos** em vez de ter **múltiplos contêineres** com **um único processo** em cada um deles.

Por exemplo (dependendo de sua configuração), você poderia ter alguma ferramenta como um exportador do Prometheus no mesmo contêiner que deve ter acesso a **cada uma das solicitações** que chegam.

# build is static and TensorFlow symbols (in Python only) are loaded into the
# global symbol table in order to support op registration. This means that
# projects building with Bazel and importing TensorFlow as a dependency will not
# depend on libtensorflow_framework.so unless they opt in.
tf_cc_shared_library(
    name = "tensorflow_framework",
    additional_linker_inputs = [
        "//tensorflow:tf_framework_version_script.lds",

    return curr_version

  print('You have bazel %s installed.' % curr_version)
  return curr_version


def set_cc_opt_flags(environ_cp):
  """Set up architecture-dependent optimization flags.

  Also append CC optimization flags to bazel.rc..

  Args:
    environ_cp: copy of the os.environ.
  """
  if is_ppc64le():

	if DB.Dialector.Name() != "sqlserver" {
		// In SQLServer, If an index or constraint depends on the column,
		// this column will not be able to run ALTER
		// see https://stackoverflow.com/questions/19460912/the-object-df-is-dependent-on-column-changing-int-to-double/19461205#19461205
		// may we need to create another PR to fix it, see https://github.com/go-gorm/sqlserver/pull/106
		tests = []TestCase{

</li>
</ol>

<p>
In all non-constant conversions involving floating-point or complex values,
if the result type cannot represent the value the conversion
succeeds but the result value is implementation-dependent.
</p>

<h4 id="Conversions_to_and_from_a_string_type">Conversions to and from a string type</h4>

<ol>
<li>
Converting a slice of bytes to a string type yields

through the value of an otherwise unused (as far as Go is concerned) register in the MMU.
In the runtime package, assembly code can include <code>go_tls.h</code>, which defines
an OS- and architecture-dependent macro <code>get_tls</code> for accessing this register.
The <code>get_tls</code> macro takes one argument, which is the register to load the
<code>g</code> pointer into.
</p>

<p>

            Doc(
                """
                A dictionary with overrides for the dependencies.

                Each key is the original dependency callable, and the value is the
                actual dependency that should be called.

                This is for testing, to replace expensive dependencies with testing
                versions.

                Read more about it in the

TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsRemapControlDependency(
    TF_ImportGraphDefOptions* opts, const char* src_name, TF_Operation* dst);

// Cause the imported graph to have a control dependency on `oper`. `oper`
// should exist in the graph being imported into.
TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsAddControlDependency(
    TF_ImportGraphDefOptions* opts, TF_Operation* oper);