corrija o seguinte erro no script do google earth engine: LULC: Layer error: No valid training data were found. Print apenas a solução indicada. Aqui está o script de exemplo: Map.setOptions('SATELLITE')////"ROADMAP", "SATELLITE", "HYBRID" or "TERRAIN" 

var Region = geometry//geometry2

var mascaraOtto = ee.Image(100).toInt().clip(Region)

var runFire = ee.List([1985,1987, 2022, 2024]).map(function(i){
  
i = ee.Number(i).toInt().format()

// Define time interval to composite scenes
  var date_start= i.cat("-06-01");
  var date_end= i.cat("-07-31");

// Define a function to scale the data and mask unwanted pixels.
function maskL457sr(image) {
  // Bit 0 - Fill
  // Bit 1 - Dilated Cloud
  // Bit 2 - Unused
  // Bit 3 - Cloud
  // Bit 4 - Cloud Shadow
  var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0);
  //var saturationMask = image.select('QA_RADSAT').eq(0);

  // Apply the scaling factors to the appropriate bands.
  var opticalBands = image.select('Red', 'NIR', 'SWIR1', 'SWIR2').multiply(0.0000275).add(-0.2);
  //var thermalBand = image.select('ST_B.*').multiply(0.00341802).add(149.0);

  // Replace the original bands with the scaled ones and apply the masks.
  return image.addBands(opticalBands, null, true)
      //.addBands(thermalBand, null, true)
      .updateMask(qaMask)
      //.updateMask(saturationMask);
}

//_________________________________________________________________________

// Define function to prepare OLI images.
function prepOLI(img) {
  img = img.select([ 'SR_B4', 'SR_B5', 'SR_B6','SR_B7', 'QA_PIXEL'],[ 'Red', 'NIR', 'SWIR1','SWIR2', 'QA_PIXEL']);
  //img = applyScaleFactors(img);
  //img = prepSrL8(img);
  img = maskL457sr(img);
  //img = renameOLI(img);
  //img = addIndices(img);
  //img = addNBR(img);
  //img = addNDVI(img);
  //img = add_MIRBI(img);
  //img = add_NBR_mask(img);
  return ee.Image(img)//.copyProperties(img, img.propertyNames()))
  .set('data',  ee.Number.parse(img.date().format("YYYYMMdd")).toInt())
  .set('anomes',  ee.Number.parse(img.date().format("YYYYMM")).toInt())
  .set('year',  ee.Number.parse(img.date().format("YYYY")).toInt())
  //.addBands(ee.Image.constant(ee.Number.parse(img.date().format("YYYYMMdd"))).toInt().rename('Date'))
  .addBands(ee.Image.constant(ee.Number.parse(img.date().format("YYYYMM"))).toInt().rename('Months'))
  //.addBands(ee.Image.constant(ee.Number.parse(img.date().format("YYYY"))).toInt().rename('Years'));
}

// Define function to prepare ETM+ images.
function prepETM(img) {
  img = img.select([ 'SR_B3', 'SR_B4','SR_B5','SR_B7', 'QA_PIXEL'],[ 'Red', 'NIR', 'SWIR1','SWIR2','QA_PIXEL']);
  //img = applyScaleFactors(img);
  //img = prepSrL8(img);
  img = maskL457sr(img);
  //img = renameETM(img);
  //img = addIndices(img);
  //img = etm2oli(img);
  //img = addNBR(img);
  //img = addNDVI(img);
  //img = add_MIRBI(img);
  //img = add_NBR_mask(img);
  return ee.Image(img)//.copyProperties(img, img.propertyNames()))
  .set('data',  ee.Number.parse(img.date().format("YYYYMMdd")).toInt())
  .set('anomes',  ee.Number.parse(img.date().format("YYYYMM")).toInt())
  .set('year',  ee.Number.parse(img.date().format("YYYY")).toInt())
  //.addBands(ee.Image.constant(ee.Number.parse(img.date().format("YYYYMMdd"))).toInt().rename('Date'))
  .addBands(ee.Image.constant(ee.Number.parse(img.date().format("YYYYMM"))).toInt().rename('Months'))
  //.addBands(ee.Image.constant(ee.Number.parse(img.date().format("YYYY"))).toInt().rename('Years'));
}


// import collections
// landsat 5 - TM
  var L5coll = ee.ImageCollection('LANDSAT/LT05/C02/T1_L2') 
  .filterBounds(Region)
  .filterDate(date_start,date_end)
  //.map(function(image){return image.clip(Region)})
  .map(prepETM);

// landsat 7 - ETM