### Docs

* 📝 Add note to docs about Separate Input and Output Schemas with FastAPI version. PR [#10150](https://github.com/tiangolo/fastapi/pull/10150) by [@tiangolo](https://github.com/tiangolo).

## 0.102.0

### Features

package etcd

import (
	"reflect"
	"testing"
)

// TestParseEndpoints - tests parseEndpoints function with valid and invalid inputs.
func TestParseEndpoints(t *testing.T) {
	testCases := []struct {
		s         string
		endpoints []string
		secure    bool
		success   bool
	}{
		// Invalid inputs
		{"https://localhost:2379,http://localhost:2380", nil, false, false},
		{",,,", nil, false, false},
		{"", nil, false, false},

                         absl::Span<AbstractTensorHandle*> outputs) {
  return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0],
                     /*transpose_a=*/false,
                     /*transpose_b=*/false, "MatMul");
}

absl::Status MulModel(AbstractContext* ctx,
                      absl::Span<AbstractTensorHandle* const> inputs,

                                    <label for="inputs" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_synonym_source"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="inputs"/>
                                        <la:textarea styleId="inputs" property="inputs" rows="5"

                               absl::Span<AbstractTensorHandle* const> inputs,
                               int input_index, bool use_function,
                               AbstractTensorHandle** numerical_grad) {
  vector<AbstractTensorHandle*> theta_inputs(inputs.size());
  for (int i{}; i < inputs.size(); ++i) {
    theta_inputs[i] = inputs[i];
  }

  AbstractTensorHandle* theta =

    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will

                        updater.write("# " + line);
                    }
                    continue;
                }

                inputs = m.group(1).trim().split(",");
                output = m.group(2).trim();

                if (inputs == null || output == null || inputs.length == 0) {
                    logger.warn("Failed to parse {} in {}", line, path);
                    if (updater != null) {

name: Latest Changes

on:
  pull_request_target:
    branches:
      - master
    types:
      - closed
  workflow_dispatch:
    inputs:
      number:
        description: PR number
        required: true
      debug_enabled:
        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
        required: false
        default: 'false'

jobs:
  latest-changes:

                                    <label for="inputs" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_mapping_source"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="inputs"/>
                                        <la:textarea styleId="inputs" property="inputs" rows="5"