// Note that we take the address of the elements in `all_input_tensors`
    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();

pkg net/http, method (*ClientConn) Err() error #75772
pkg net/http, method (*ClientConn) InFlight() int #75772
pkg net/http, method (*ClientConn) Release() #75772
pkg net/http, method (*ClientConn) Reserve() error #75772
pkg net/http, method (*ClientConn) RoundTrip(*Request) (*Response, error) #75772
pkg net/http, method (*ClientConn) SetStateHook(func(*ClientConn)) #75772

    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {
      dim_vec.push_back(ic->MakeDim(dims[i]));
    }
    return ic->MakeShape(dim_vec);
  } else {
    return ic->UnknownShape();
  }
}

  return grads;
}

std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {
  std::vector<string> names;
  auto functions = graph_def.library().function();
  names.reserve(functions.size());
  for (const tensorflow::FunctionDef& func : functions) {
    names.push_back(func.signature().name());
  }
  std::sort(names.begin(), names.end());
  return names;
}

* «the worker process»: NOT «der Arbeiterprozess»
* «to commit»: «committen»
* «to deploy» (in the cloud): «deployen»
* «to modify»: «ändern»
* «to serve» (an application): «bereitstellen»
* «to serve» (a response): «ausliefern»
* «to serve»: NOT «bedienen»
* «to upgrade»: «aktualisieren»
* «to wrap»: «wrappen»
* «to wrap»: NOT «hüllen»
* «`foo` as a `type`»: «`foo` vom Typ `type`»

* `@app.get()`
* `@app.post()`
* `@app.put()`
* `@app.delete()`
* etc.

{* ../../docs_src/response_status_code/tutorial001_py39.py hl[6] *}

/// note | Nota

Observe que `status_code` é um parâmetro do método "decorador" (`get`, `post`, etc). Não da sua função de *operação de rota*, como todos os parâmetros e corpo.

///

O parâmetro `status_code` recebe um número com o código de status HTTP.

                    + "Mixins are not supported in the default consumer POM format. "
                    + "You have the following options to resolve this:" + System.lineSeparator()
                    + "  1. Preserve the model version by setting 'preserve.model.version=true' to generate a consumer POM with <modelVersion>4.2.0</modelVersion>, which supports mixins"
                    + System.lineSeparator()

# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

// Copyright 2025 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

TEXT errors(SB),$0
	VSHUF4IV	$16, V1, V2	// ERROR "operand out of range 0 to 15"
	XVSHUF4IV	$16, X1, X2	// ERROR "operand out of range 0 to 15"
	ADDV16		$1, R4, R5	// ERROR "the constant must be a multiple of 65536."
	ADDV16		$65535, R4, R5	// ERROR "the constant must be a multiple of 65536."

# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,