Georgia Craciun: A Comprehensive Analysis for Sports Betting Enthusiasts

Overview / Introduction

Georgia Craciun, a rising star in the tennis world, is a Romanian professional player known for her dynamic playing style and competitive spirit. Born on October 5, 1998, she currently plays as a singles player. Her career has been marked by steady progress through the ranks, making her an intriguing choice for sports betting enthusiasts.

Career Achievements and Statistics

Georgia Craciun has made significant strides in her career, with notable achievements including reaching the quarterfinals at the ITF Women’s Circuit. As of now, she holds a WTA ranking that reflects her growing prowess on the court. Her recent matches have shown a mix of wins and losses, with key victories against top-100 players boosting her confidence and ranking.

Playing Style and Key Strengths

Craciun’s playing style is characterized by her aggressive baseline play and powerful groundstrokes. Her ability to dictate points with her forehand makes her a formidable opponent. Additionally, her strategic use of drop shots keeps opponents off-balance.

Interesting Facts and Unique Traits

Nicknamed “The Romanian Rocket,” Georgia Craciun has garnered a fanbase appreciative of her energetic performances. Known for her resilience and positive attitude on court, she often engages with fans post-match, enhancing her popularity.

Lists & Rankings of Performance Metrics or Top Stats

• ✅ Strong baseline game
• 🎰 Recent win against top-100 player
• 💡 Consistent improvement in serve accuracy

Comparisons with Other Players in the Same Team or League

In comparison to other Romanian players in the WTA circuit, Georgia stands out due to her aggressive playstyle and rapid ascent through the ranks. While others may focus on defensive strategies, Craciun’s offensive approach sets her apart.

Player-Focused Case Studies or Career Stories

A pivotal moment in Craciun’s career was her breakthrough performance at the ITF tournament in Italy, where she advanced to the semifinals. This match highlighted her potential to compete against higher-ranked players.

Tables Summarizing Statistics, Recent Form, Head-to-Head Records, or Odds

Tournament	Date	Opponent	Result
Tennis Open Italia Women’s Tournament	2023-05-15	Maria Sakkari (ITA)	Lost (QF)

Tips & Recommendations for Analyzing the Player or Betting Insights 💡 Advice Blocks 📝 Tips 💡 Advice Blocks 📝 Tips 💡 Advice Blocks 📝 Tips 💡 Advice Blocks 📝 Tips 💡 Advice Blocks 📝 Tips 💡 Advice Blocks 📝 Tips 💡 Advice Blocks 📝 Tips 💡 Advice Blocks 📝 Tips

To effectively analyze Georgia Craciun for betting purposes:

• Analyze recent match performances to gauge current form.
• Consider head-to-head records against upcoming opponents.
• Maintain awareness of any injuries that could impact performance.
• Familiarize yourself with betting odds trends related to similar players.

Quotes or Expert Opinions about the Player (Quote Block)

“Georgia Craciun’s tenacity on court is remarkable; she consistently pushes boundaries and surprises opponents,” says renowned tennis analyst John Doe.

The Pros & Cons of Georgia’s Current Form or Performance (✅❌ Lists)

• ✅ Strong forehand shots that dominate rallies.
• ✅ Excellent mental fortitude under pressure.

• ❌ Inconsistent service game needs improvement.
• ❌ Occasional lapses in concentration during long matches.</li
  [0]: # Copyright (c) Facebook, Inc. and its affiliates.
  [1]: #
  [2]: # This source code is licensed under the MIT license found in the
  [3]: # LICENSE file in the root directory of this source tree.

  [4]: import torch

  [5]: from fairseq import metrics
  [6]: from fairseq.criterions import FairseqCriterion

  [7]: def compute_kl_loss(model,
  [8]: net_output,
  [9]: pad_mask,
  [10]: reduce=True):
  [11]: """
  [12]: Compute KL loss between posterior distribution p(z|x) defined by
  [13]: encoder outputs `net_output.encoder_out` and prior distribution p(z)
  [14]: defined by `prior_out`.

  [15]: Args:
  [16]: model (`~fairseq.models.FairseqModel`): Fairseq model instance.
  [17]: net_output: Tuple with `encoder_out` packed inside.
  [18]: – **encoder_out** — A tuple with two elements:
  [19]: * **encoder_out** — Tensor containing the encoded features
  [20]: `[src_len x batch x encoder dim]`
  [21]: * **encoder_padding_mask** — ByteTensor `[batch x src_len]`
  [22]: indicating padding elements.

  [23]: pad_mask (`torch.Tensor`):
  [24]: Mask used to avoid computing loss on padded elements.

  Shape:

  `(batch size)`

  Type:

  `torch.ByteTensor`

  Default:

  None

  reduce (`bool`, *optional*, default=True):
  If set to “True“, will call “mean()“ on
  KL divergence before returning.

  Returns:

  `tuple` with two elements:

  – **loss**: scalar giving loss value

  – **sample_size**: number of elements in summation

  Returns:

  `tuple`: Tuple comprising

  – **loss**: scalar giving loss value

  – **sample_size**: number of elements in summation

  Shorthand for::

  return compute_kl_loss(
  model=model,
  net_output=net_output,
  pad_mask=pad_mask,
  reduce=reduce)

  """

  encoder_out = net_output.encoder_out

  kl_divergence = model.get_kl_divergence(encoder_out)

  if pad_mask is not None:

  kl_divergence = kl_divergence.masked_fill(
  ~pad_mask[:, :, None], float("-inf"))

  if reduce:

  loss = kl_divergence.sum()

  else:

  loss = kl_divergence

  sample_size = pad_mask.sum() if pad_mask is not None else
  kl_divergence.size(0)

  return loss / sample_size.float(), sample_size

  ***** Tag Data *****
  ID: 1
  description: Function definition for computing KL divergence loss between posterior
  distribution p(z|x) defined by encoder outputs and prior distribution p(z). It involves
  tensor manipulations using PyTorch functions such as masked_fill.
  start line: 7
  end line: 51
  dependencies:
  – type: Function
  name: compute_kl_loss
  start line: 7
  end line: 51
  context description: This function computes KL divergence which is crucial for models
  involving variational inference like VAEs (Variational Autoencoders). Understanding
  this function requires knowledge about probabilistic models and tensor operations.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: '5'
  self contained: Y

  ************
  ## Challenging aspects

  ### Challenging aspects in above code

  1. **KL Divergence Computation**: The computation involves understanding both variational inference principles (like VAEs) and practical implementation details such as handling tensors correctly within PyTorch.

  2. **Handling Padded Sequences**: The use of `pad_mask` introduces complexity since it requires careful masking operations to ensure that padded sequences do not contribute to the loss computation.

  3. **Conditional Reduction**: The reduction step needs conditional logic based on whether we want mean reduction or not. This adds complexity because it affects how you aggregate results across batches.

  4. **Dynamic Shapes**: Handling tensors dynamically based on input shapes can be tricky especially when dealing with masks that might vary per batch.

  5. **Efficient Tensor Operations**: Ensuring efficient tensor operations while maintaining readability can be challenging given PyTorch’s broadcasting rules and memory management nuances.

  ### Extension

  1. **Handling Multiple Distributions**: Extend functionality so that it can handle multiple distributions simultaneously instead of just one posterior-prior pair.

  2. **Batch-wise Variability**: Introduce variability within batches where each element might have different mask lengths or different prior distributions.

  3. **Incorporating Additional Loss Components**: Combine KL divergence with other types of losses such as reconstruction losses typical in VAEs.

  4. **Parallel Processing Support**: Adapt code to support parallel processing over distributed systems while ensuring synchronization issues are managed correctly.

  5. **Customizable Reduction Strategies**: Allow users to specify custom reduction strategies beyond just mean reduction.

  ## Exercise

  ### Problem Statement

  You are tasked with extending an existing function that computes KL divergence between a posterior distribution ( p(z|x) ) defined by encoder outputs (`net_output.encoder_out`) and a prior distribution ( p(z) ). Your goal is to:

  1. Modify [SNIPPET] so it can handle multiple posterior-prior pairs simultaneously.

  2. Introduce support for custom reduction strategies provided by users as callable functions.

  3. Incorporate an additional reconstruction loss component which should be added optionally based on user input.

  4. Ensure your implementation supports efficient parallel processing over distributed systems using PyTorch’s distributed package.

  ### Requirements:

  * Modify [SNIPPET] so it accepts an additional argument `multiple_prior_outputs` which contains multiple prior distributions corresponding to each posterior distribution.
  * Introduce another argument `custom_reduction_fn` which allows users to pass their own reduction strategy function.
  * Add an optional argument `reconstruction_loss_fn` which takes another tensor representing reconstruction targets and computes an additional reconstruction loss component when provided.
  * Ensure your implementation efficiently handles these operations across distributed systems using PyTorch’s distributed package functionalities like DistributedDataParallel (DDP).

  python
  # [SNIPPET]

  def compute_kl_loss_extended(model,
  net_output,
  pad_mask=None,
  multiple_prior_outputs=None,
  custom_reduction_fn=None,
  reconstruction_loss_fn=None,
  dist_sync_on_step=False):
  """
  Extended version of compute_kl_loss function handling multiple priors,
  custom reduction strategies, additional reconstruction losses,
  and efficient parallel processing support using PyTorch DDP.

  Args are similar but include new ones explained below:

  Args:
  multiple_prior_outputs (`List[Tensor]`, optional): List containing multiple prior distributions
  corresponding to each posterior distribution.
  Defaults to None if single prior is used.
  custom_reduction_fn (`Callable`, optional): Custom function provided by user
  specifying how reduction should be done over
  computed divergences across batches/tensors.
  Defaults to mean reduction if None.
  reconstruction_loss_fn (`Callable`, optional): Function taking reconstructed targets
  tensor along with original targets tensor
  computing additional reconstruction loss component.
  Defaults to None if no extra reconstruciton loss required.
  dist_sync_on_step (`bool`, optional): Whether gradients should be synchronized after each step during DDP training phase?
  Defaults False but recommended True when using DDP training setup

  Returns updated tuple including combined total loss incorporating all components described above

  """

  ## Solution

  python

  import torch.distributed as dist

  def compute_kl_loss_extended(model,
  net_output,
  pad_mask=None,
  multiple_prior_outputs=None,
  custom_reduction_fn=None,
  reconstruction_loss_fn=None,
  dist_sync_on_step=False):

  encoder_out = net_output.encoder_out

  # Assume model.get_kl_divergence returns list when multiple priors are passed
  kl_divergences = model.get_kl_divergence(encoder_out)

  if isinstance(kl_divergences, list):
  assert len(kl_divergences) == len(multiple_prior_outputs),
  "Number of posteriors must match number of priors"
  combined_kl_dvrgnce = torch.stack([kl.diverge(prior)
  for kl,prior in zip(kl_divergences,multiple_prior_outputs)])

  if pad_mask is not None:
  combined_kl_dvrgnce = combined_kl_dvrgnce.masked_fill(
  ~pad_mask[:, :, None], float("-inf"))

  # Apply custom reduction function if provided otherwise default sum/mean reductions applied

  if custom_reduction_fn is not None:
  reduced_loss = custom_reduction_fn(combined_kl_dvrgnce)
  else :
  reduced_loss = combined_kl_dvrgnce.sum()

  reduced_sample_size = pad_mask.sum() if pad_mask is not None else
  combined_kl_dvrgnce.size(0)

  total_loss=reduced_loss/reduced_sample_size.float()

  total_sample_size=reduced_sample_size

  elif isinstance(kl_divergences,(torch.Tensor)):

  assert len(multiple_prior_outputs)==1,"Multiple priors expected only when list returned"

  combined_kldiv=kl.diverge(multiple_prior_outputs.pop())

  assert combined_kldiv.dim()==kl.diverge(dim)==len(padmask.dim())+1,
  "Mismatched dimensions detected"

  assert combined_padmask.dim()==padmask.dim()+1,
  "Mismatched dimensions detected"

  assert combined_padmask.size(0)==combined_padmask.size(0),
  "Batch size mismatch detected"

  assert combined_padmask.size(1)==combined_padmask.size(1),
  "Sequence length mismatch detected"

  .
  .
  .
  . ## Further Detailed Implementation

  # Handle Reconstruction Loss Component Addition

  if reconstruction_loss_fn is not None :
  reconstrn_target_tensor=torch.randn_like(encoderout)
  reconstrn_target_tensor.reconstruction_targets(targets,reconstrn_target_tensor)
  reconstrn_total_losses=reconstructionlossfn(reconstrn_target_tensor,targts)
  total_losses+=reconstrn_total_losses

  return total_losses,total_sample_sizes

  ## Follow-up exercise

  ### Problem Statement

  Extend your solution further by adding support for dynamically adjusting learning rates based on batch-wise KL divergence values observed during training iterations:

  * Implement logic within your extended function such that learning rates are adjusted dynamically based on moving average calculations over observed KL divergences from previous batches processed within current epoch iteration cycle across GPUs/Nodes involved via DistributedDataParallel (DDP).
  * Ensure this dynamic adjustment mechanism operates smoothly without causing race conditions during multi-GPU setups via proper synchronization mechanisms available within PyTorch DDP framework.

  ## Solution

  python

  import torch.optim.lr_scheduler as lr_scheduler

  class DynamicLRScheduler():

  def __init__(self,optimal_learning_rate,min_learning_rate,max_learning_rate,batch_wise_moving_average_window):

  self.optimal_lr=optimal_learning_rate

  self.min_lr=min_learning_rate

  self.max_lr=max_learning_rate

  self.moving_average_window=batch_wise_moving_average_window

  self.kldiv_moving_averages=[]

  def update_lr(self,current_batch_idx,model_optimizer_instance,current_batch_avg_KL_Divergence):

  moving_average=self.kldiv_moving_averages[-self.moving_average_window:]
  moving_average.append(current_batch_avg_KL_Divergence)

  moving_avg_val=sum(moving_average)/len(moving_average)

  optimal_adjustment_factor=moving_avg_val/self.optimal_lr

  new_lr=max(min(self.max_lr,self.optimal_lr*optimal_adjustment_factor),self.min_lr)

  model_optimizer_instance.param_groups=[{'lr':new_lr}]

  def integrate_with_ddp_training_loop():
  optimizer_instance=optim.Adam(model.parameters(),lr=self.optimal_lr)

  scheduler_instance=DynamicLRScheduler(optimal_learning_rate=self.optimal_lr,min_learning_rate=self.min_lr,max_learning_rate=self.max_lr,batch_wise_moving_average_window=10)

  ddp_model_wrapped=torch.nn.parallel.DistributedDataParallel(model)

  ddp_model_wrapped.to(device)

  train_loader=train_dataset_loader(batchsize=batchsize,dataloader_workers=dataloader_workers,num_workers=num_workers)

  for epoch_idx,in_epoch_batches_iterator:

  for batch_idx,batch_data_tuple_in_epoch_iterator:

  optimizer_instance.zero_grad()

  model_forward_pass_inputs=batch_data_tuple_in_epoch_iterator

  output=model_forward_pass_inputs.forward()

  kldiv_value=output.kldiv()

  scheduler_instance.update_lr(current_batch_idx=batch_idx,model_optimizer_instance=optimizer_instance,current_batch_avg_KL_Divergence=kldiv_value.mean())

  output.backward()

  optimizer.step()

  if(dist.is_initialized()):
  dist.barrier()
  dist.all_reduce(torch.tensor(output.loss).to(device))
  dist.all_reduce(torch.tensor(output.sample_size).to(device))

  return output.loss,output.sample_size

  *** Excerpt ***

  *** Revision 0 ***

  ## Plan

  To create an exercise that would be as advanced as possible while requiring profound understanding and additional factual knowledge beyond what's presented directly in an excerpt:

  1. The excerpt itself must present complex information that necessitates background knowledge spanning several disciplines—such as history, philosophy, science—and requires understanding nuanced arguments or theories relevant to these fields.

  2. Incorporate deductive reasoning within the excerpt by presenting premises leading logically towards conclusions but doing so through complex sentence structures involving nested conditionals ("If…then…if…then") or counterfactuals ("Had X happened…Y would have been…").

  3. Embed subtle logical steps that readers must follow closely; these could involve identifying implicit assumptions or recognizing unstated implications critical for drawing conclusions from what's presented explicitly.

  By rewriting an excerpt according to these guidelines—making it dense with information requiring cross-disciplinary knowledge while embedding complex logical structures—the resulting exercise will challenge even those well-versed in critical thinking exercises.

  ## Rewritten Excerpt

  In contemplating the hypothetical scenario wherein Archimedes had access not only to his classical lever but also possessed insights into modern quantum mechanics—a discipline far removed temporally from his era—it becomes conceivable that he might have postulated a theorem integrating both classical mechanics' deterministic nature with quantum mechanics' probabilistic underpinning principles; had he conceived such integration successfully before his untimely demise at Syracuse's siege—a counterfactual premise indeed—this amalgamation might have accelerated technological advancements centuries ahead of their historical timeline; thus positing Archimedes at this intersection would necessitate acknowledging his potential contributions beyond mere mathematical formulations into realms touching upon early conceptualizations of quantum phenomena; however contingent upon this hypothetical scenario being plausible rests heavily upon one accepting several layers of counterfactual reasoning intertwined with speculative historical analysis—a task undoubtedly daunting yet intriguingly speculative at best.

  ## Suggested Exercise

  Given Archimedes' theoretical engagement with both classical mechanics principles through his invention of levers alongside speculative insights into quantum mechanics' probabilistic nature—an intellectual endeavor resting upon numerous layers of counterfactual reasoning—consider how this hypothetical amalgamation could potentially alter historical technological advancements trajectory:

  A) It would invalidate all known historical developments until proven otherwise through archaeological findings supporting advanced ancient technologies directly attributable to Archimedes' theoretical work.

  B) It suggests no significant alteration since technological advancements depend solely on empirical experimentation rather than theoretical speculation alone; hence Archimedes' hypothetical contributions would remain purely academic without practical application until much later periods conducive environments were established.

  C) It implies a substantial acceleration in technological development timelines owing primarily to Archimedes' supposed early conceptualization bridging deterministic classical mechanics principles with quantum mechanics’ probabilistic underpinnings—a fusion potentially laying groundwork centuries ahead its time had it been actualized before his death during Syracuse's siege.

  D) It necessitates reevaluation only concerning philosophical implications regarding determinism versus probability without necessarily impacting tangible technological advancements directly attributed solely to Archimedes’ theoretical propositions.

  *** Revision 1 ***

  check requirements:
  – req_no: 1
  discussion: The draft does not require specific external knowledge beyond generalities;
  no specific academic fact or theory outside general knowledge about quantum mechanics
  or historical context is needed.
  score: 0
  – req_no: '2'
  discussion': Understanding subtleties like 'counterfactual reasoning' embedded deeply;
  however more direct relation between choices should reflect nuances better.'
  score: '2'
  – req_no': '3'
  discussion': The excerpt meets length requirement but could use more complexity or
  embedded conditional statements linking directly back into choices.'
  score': '2'
  – req_no': '4'
  discussion': Choices are misleading but could benefit from closer ties back into specifics/nuances/details mentioned explicitly/explained implicitly within excerpt.'
  ? correct choice needs clearer tie-back into nuanced understanding gained specifically/from/excerpt content/specific contextual understanding?
  revision suggestion": To enhance connection between external knowledge requirement (#1), consider asking how certain real-world applications influenced by quantum mechanics might differ had they been developed earlier due influenced theoretically by Archimedes’ hypothesized work described here—requiring specific knowledge about those applications today! Adjust choices so they reflect subtler distinctions drawn from understanding both excerpt content deeply AND real-world implications tied closely."
  revised exercise": Considering how modern-day applications influenced by quantum mechanics might differ had they been developed earlier due theoretically influenced by Archimedes’ hypothesized integration described above—evaluate which statement most accurately reflects potential impacts detailed intricately within our discussion?
  incorrect choices":
  – Quantum computing would likely have emerged during classical antiquity leading directly…
  correct choice": Technological development timelines would substantially accelerate owing…
  other choices":
  – Historical developments up till now remain unchanged despite early theoretical advances…
  *** Revision ***
  science_fictional_concept_discussion": "Archimedes integrating classical mechanics principles like levers along side modern quantum mechanical concepts like probabilistic phenomena represents a science fiction scenario since there was no concept equivalent quantum physics during his time.", "external_fact": "Knowledge about how actual historical scientific progress occurred especially relating discoveries transitioning from classical physics towards modern physics", "revision suggestion": "To fulfill requirement #1 better regarding external academic facts necessary for solving exercise properly aligning answers more precisely towards nuanced comprehension derived from excerpt content itself plus required external knowledge around historical scientific progress transitions; revise question prompt focusing specifically on comparing speculated outcomes versus actual historic scientific developments particularly highlighting contrasts between deterministic views typical pre-modern physics era vs probabilistic nature inherent modern physics.", "revised exercise": ""Considering historically documented transitions from deterministic classical physics approaches towards probabilistic interpretations characterizing modern physics including Quantum Mechanicsu2014assess how differently technological evolution might have unfolded had figures like Archimedes pioneered theories blending these paradigms earlier than historically recorded? Evaluate which statement most accurately captures potential impacts considering detailed discussions above."", "correct choice": ""Technological development timelines would substantially accelerate owing primarily due early conceptualization bridging deterministic classical mechanics principles with quantum mechanicsu2019 probabilistic underpinnings."", "incorrect choices": ["Quantum computing would likely have emerged during classical antiquity leading directly…", ""Historical developments up till now remain unchanged despite early theoretical advances…"]"}
  compiled_metrics::CompiledMetrics {
  compiled_metrics::CompiledMetrics {
  ..Default::default()
  }
  };

  let mut compiled_metrics_mut =
  compiled_metrics.clone();

  let mut metrics_iter =
  iter::empty::().fuse();

  // We expect metrics_iter.next() below will return Some(&metrics),
  // because we just put metrics into compiled_metrics_mut.metrics(),
  // so we need metrics_iter.next().unwrap() here first before calling next().
  let next_ret =
  metrics_iter.next().unwrap().next();

  let next_ret_expected =
  Some(metrics.clone());

  assert_eq!(next_ret.unwrap(), next_ret_expected);

  // After calling next(), we expect metrics_iter.next() will return none().
  let next_ret_after_next =
  metrics_iter.next();

  assert_eq!(next_ret_after_next.unwrap(), None);
  }

  #[test]
  fn test_compiled_metrics_next_cannot_be_called_again_if_already_return_none() {
  let mut compiled_metrics =
  compiled_metrics::CompiledMetrics {
  ..Default::default()
  };

  let mut metrics_iter =
  iter::empty::().fuse();

  // We expect metrics_iter.next() below will return Some(&metrics),
  // because we just put metrics into compiled_metrics.metrics(),
  // so we need metrics_iter.next().unwrap() here first before calling next().
  // TODO(#2368): Fix test compilation error after fixing issue https://github.com/rust-lang/rust/issues/58049/
  // let _next_ret =
  // metrics_iter.next().unwrap().next();
  //
  // let _next_ret_after_next =
  // metrics_iter.next();
  //
  // panic!(“This test should never get here!”);
  }

  #[test]
  fn test_compiled_metrics_get_metric_by_name_and_tag_return_none_if_not_found() {
  // TODO(#2368): Fix test compilation error after fixing issue https://github.com/rust-lang/rust/issues/58049/
  // let mut compiled_metrics =
  // compiled_metrics::CompiledMetrics {
  // ..Default::default()
  // };
  //
  // let metric_name_not_exist =
  // String::from(“metric_name_not_exist”);
  //
  // let tags_not_exist_str_array =
  // vec![String::from(“tag_name_not_exist”), String::from(“tag_value_not_exist”)];
  //
  // let tags_not_exist_str_array_slice =
  // &tags_not_exist_str_array[..];
  //
  // let tags_not_exist_vec_of_tuples_array_slice =
  // &[(“tag_name_not_exist”, String::from(“tag_value_not_exist”))];
  //
  // // Test case one : metric name does not exist => Should return none().
  //
  //// TODO(#2368): Fix test compilation error after fixing issue https://github.com/rust-lang/rust/issues/58049/
  //// println!(
  //// “nnTest case one : metric name does NOT exist => Should return none()nn”,
  //// );
  //
  //// TODO(#2368): Fix test compilation error after fixing issue https://github.com/rust-lang/rust/issues/58049/
  //// println!(
  //// “ttGet metric {} via tag array {:?} => Should return none()n”,
  //// metric_name_not_exist.as_ref(),
  //// tags_not_exist_str_array_slice.as_ref(),
  //// );
  ////
  //// TODO(#2368): Fix test compilation error after fixing issue https://github.com/rust-lang/rust/issues/58049/
  //// println!(
  //// “ttGet metric {} via tag array {:?} => Should return none()n”,
  //// metric_name_not_exist.as_ref(),
  //// tags_not_exist_vec_of_tuples_array_slice.as_ref(),
  ////
  //// );
  ////
  ////
  ////
  ////
  ////
  ////
  ////
  ////
  ////
  ////
  ////
  ////
  ////
  //////// TODO(#2368): Fix test compilation error after fixing issue https://github.com/rust-lang/rust/issues/58049/
  //////// println!(
  //////// “ttGet metric {} via tag array {:?} => Should return none()n”,
  //////// metric_name_not_exist.as_ref(),
  //////// tags_not_exist_str_array_slice.as_ref(),
  //////// );
  //
  //
  //
  //
  //
  //

  //- #[test]
  //- fn test_compiled_metrics_get_metric_by_name_and_tag_return_none_if_metric_doesnt_have_tags_but_tags_provided() {
  //- //
  //- //
  //- //
  //- //
  //- //
  //- //
  //- //
  //- //
  //- //
  //- //
  //- //

  let mut counters_to_be_put_into_compiled_metric_vec_for_testing_get_metric_by_tag_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tags_vector_of_tuples_test_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tags_test_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tags_test_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tags_test_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tagstest_case_one_vector_of_tuplestest_case_two_stringsof_keysandvaluesdo_matchbutorderisdifferentsoitssupposedtotrusttheorderofprovidedtagsandnottheorderoflabelkeynamesincounterlabels_testcaseonevectoroftupleswithtagsbutcounterdoesnthavetagsorlabelkeynamesandvaluesaredifferentfromtheprovidedtagsvectoroftuples_testcaseonevectoroftupleswithtagsbutcounterdoesnthavetagsorlabelkeynamesandvaluesaredifferentfromtheprovidedtags_testcaseonevectoroftupleswithtagsbutcounterdoesnthavetagsorlabelkeynamesandvaluesaredifferentfromtheprovidedtags_testcaseonevectoroftupleswithtagsbutcounterdoesnthavetagsorlabelkeynamesandvaluesaredifferentfromtheprovidedtags_testcaseonevectoroftupleswithtagsbutcounterdoesnthavetagsorlabelkeynamesandvaluesaredifferentfromtheprovidedtagstestcaseonevectoroftuplestestcasetwostringsofkeysandvaluesdo_matchbutorderisdifferentsoitssupposedtotrusttheorderofprovidedtagsandnottheorderoflabelkeynamesincounterlabels_testcaseonestringsofkeysandvaluesthatmatchwhentheyareputintoastringarray_doctest_cannot_compile_when_using_strings_instead_of_string_slices__issue_2366_fixes_this_problem__because_i_am_just_a_unit_test_so_i_can_use_strings_instead_of_string_slices_here__its_only_a_workaround_for_now_until_i_can_fix_my_other_tests_that_cannot_compile_because_they_use_strings_instead_of_string_slices__which_is_caused_by_issue_2366_that_prevents_me_from_using_string_slices_in_my_tests_so_im_forced_to_use_strings_instead_to_make_my_tests_compile__even_tho_it_is_an_unsafe_workaround_until_issue_2366_is_fixed_someday_in_future_somehow__: Vec;
  let mut gauges_to_be_put_into_compiled_metric_vec_for_testing_get_metric_by_tag_case_two_stringsof_keysandvaluesdo_matchbutorderisdifferentsoitssupposedtotrusttheorderofprovidedtagsandnottheorderoflabelkeynamesincounterlabels_testcaseonestringsofkeysandvaluesthatmatchwhentheyareputintoastringarray_doctest_cannot_compile_when_using_strings_instead_of_string_slices__issue_2366_fixes_this_problem__because_i_am_just_a_unit_test_so_i_can_use_strings_instead_of_string_slices_here__its_only_a_workaround_for_now_until_i_can_fix_my_other_tests_that_cannot_compile_because_they_use_strings_instead_of_string_slices__which_is_caused_by_issue_2366_that_prevents_me_from_using_string_slices_in_my_tests_so_im_forced_to_use_strings_instead_to_make_my_tests_compile__even_tho_it_is_an_unsafe_workaround_until_issue_2366_is_fixed_someday_in_future_somehow__: Vec;
  let mut histograms_to_be_put_into_compiled_metric_vec_for_testing_get_metric_by_tag_case_three_histogram_has_no_labels_but_histogram_has_labels_whose_keys_and_values_match_the_provided_tag_keys_and_values_doctest_cannot_compile_when_using_strings_instead_of_string_slices__issue_2366_fixes_this_problem__because_i_am_just_a_unit_test_so_i_can_use_strings_instead_of_string_slices_here__its_only_a_workaround_for_now_until_i_can_fix_my_other_tests_that_cannot_compile_because_they_use_strings_instead_of_string_slices__which_is_caused_by_issue_2366_that_prevents_me_from_using_string_slices_in_my_tests_so_im_forced_to_use_strings_instead_to_make_my_tests_compile__even_tho_it_is_an_unsafe_workaround_until_issue_2366_is_fixed_someday_in_future_somehow__: Vec;

  let counters_to_be_put_into_compiled_metric_vec_for_testing_get_metric_by_tag_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tags_vector_of_tuples_test_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tags_test_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tags_test_case_one_vector_of_tuples_with_tags_but_counter_doesnt_have_tags_or_label_key_names_and_values_are_different_from_the_provided_tags_test_case_one_vector_of_tuples_with_tags_but_counter_doesnthavetagsorlabelkeynamesandvaluesaredifferentfromtheprovidedtagstestcaseonevectoroftuplestestcasetwostringsofkeysandvaluesdo_matchbutorderisdifferentsoitssupposedtotrusttheorderofprovidedtagsandnottheorderoflabelkeynamesincounterlabels_TestCaseOneVectorOfTuplesWithTagsButCounterDoesntHaveTagsOrLabelKeyNamesAndValuesAreDifferentFromTheProvidedTagsVectorOfTuplesTestCaseOneVectorOfTuplesWithTagsButCounterDoesntHaveTagsOrLabelKeyNamesAndValuesAreDifferentFromTheProvidedTagsTestCaseOneVectorOfTuplesWithTagsButCounterDoesntsHaveTagsOrLabelKeyNamesAndValuesAreDifferentFromTheProvidedTagsTestCaseOneVectorOfTuplesWithTagsButCounterDoesntsHaveTagSOrLabelKeyNamesAndValuesAreDifferentFromTheProvidedTagStestCaseOneVectorOfTupleStestCaseTwoStringSoFKeysAndValueSthatMatchWhenTheyArePutIntoAStringArray_DoCTESTCannotCompileWhenUsingStringsInsteadOfStringSlices_Issue238FixesThisProblemBecauseIamJustAUnitTestSoICanUseStringsInsteadOfStringSlicesHereItsOnlyAWorkAroundForNowUntilICanFixMyOtherTestsThatCannotCompileBecauseTheyUseStringsInsteadOfStringSlicesWhichIsCausedByIssue238ThatPreventsMeFromUsingStringSlicesInMyTestsSoImForcedToUseStringsInsteadToMakeMyTestsCompileEvenThoItIsAnUnsafeWorkAroundUntilIssue238IsFixedSomedayInFutureSomehow;

  counters_to_be_put_into_compiled_metric_vec_for_testing_get_metric_by_tag_case_one_vector_of_tuples_with_tags_but_counter_doesnts_have_t_ag_or_label_key_n_am_es_and_v_alues_ar_ediff_fro_m_t_he_p_rovi_de_d_t_ag_s_v_ec_t_o_f_t_u_p_le_st_c_as_e_o_ne_v_ec_t_o_f_t_u_p_le_st_c_as_e_o_ne_v_ec_t_o_f_t_u_p_le_st_c_as_e_o_ne_v_ec_t_o_f_t_u_p_le_st_c_as_e_o_ne_v_ec_t_o_f_t_u_p_le_st_c_as_e_o_ne_v_ec_t_o_f_t_u_ple_st_CaseTwoStringSoFKeysAndValueSthatMatchWhenTheyArePutIntoAStringArray_DoCTESTCannotCompileWhenUsingStringsInsteadOfStringSlices_Issue238FixesThisProblemBecauseIamJustAUnitTestSoICanUseStringsInsteadOfStringSlicesHereItsOnlyAWorkAroundForNowUntilICanFixMyOtherTestsThatCannotCompileBecauseTheyUseStringsInsteadOfStringSlicesWhichIsCausedByIssue238ThatPreventsMeFromUsingStringSlicesInMyTestsSoImForcedToUseStringsInsteadToMakeMyTestsCompileEvenThoItIsAnUnsafeWorkAroundUntilIssue238IsFixedSomedayInFutureSomehow.push(
  CounterBuilder::new(
  “test-metric-counter-with-labels-and-tags-for-testing-get-metric-by-tag-case-one-vector-of-tuples-with-tags-but-counter-does-not-have-tags-or-label-key-names-and-values-differ-from-the-provide-d-tags-vector-of-tuples-test-case-one-vector-of-tuple-s-with-tags-but-counter-does-not-have-tags-or-label-key-names-and-values-differ-from-the-provide-d-tags-test-case-one-vector-of-tuple-s-with-tags-but-counter-does-not-have-tags-or-label-key-names-and-values-differ-from-the-provide-d-tags-test-case-one-vector-of-tuple-s-with-tags-but-counter-does-not-have-tag-s-or-label-key-n-am-es-and-v-alues-ar-ediff-fro-m-t-he-p-rovi-de-d-t-ag-stest-case-one-vector-of-tu-p-le-st_CaseTwoStringSoFKeysAndValueSthatMatchWhenTheyArePutIntoAStringArray_DoCTESTCannotCompileWhenUsingStringsInste-adOfStringSl-c-es_Issue238FixesThisProblemBecauseIamJustAUnitTestSoICanUs-eStri-ngsInst