RoKi Ice-Hockey Team: A Comprehensive Guide for Sports Bettors

Overview of RoKi: Country, League, and Formation

The RoKi ice-hockey team hails from [Country/Region], competing in the prestigious [League Name]. Founded in [Year Founded], the team is currently managed by [Coach/Manager]. Known for their dynamic play and strategic depth, RoKi has established itself as a formidable contender in the league.

Team History and Achievements

RoKi boasts a rich history with numerous accolades. The team has clinched titles such as [Title 1] and [Title 2], and consistently ranks among the top in league standings. Notable seasons include [Season Year], where they achieved an impressive record of [Record].

Current Squad and Key Players

The current squad features standout players like [Player 1] (Position) and [Player 2] (Position). Known for their exceptional skills, these key players contribute significantly to the team’s performance.

Team Playing Style and Tactics

Roki employs a versatile formation that emphasizes both offensive prowess and defensive solidity. Their strategies focus on quick transitions and maintaining possession, leveraging strengths such as speed and agility while addressing weaknesses like occasional lapses in defense.

Interesting Facts and Unique Traits

• Nickname: “The Ice Warriors”
• Fanbase: Known for their passionate support, often filling arenas to capacity.
• Rivalries: Intense matchups with teams like [Rival Team], adding excitement to each game.
• Traditions: Celebrates victories with unique post-game rituals that have become iconic.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Top Scorer: Player A – Goals: 25 | Assists: 18 | Points: 43
• ❌ Defensive Weakness: Opponents’ goals conceded per game: 2.5
• 🎰 Key Player: Player B – Playmaking ability crucial in tight matches.
• 💡 Rising Star: Player C – Young talent showing promising potential.

Comparisons with Other Teams in the League or Division

Roki stands out against competitors due to their balanced attack and solid defense. Compared to rivals like [Competitor Team], they excel in maintaining control during critical phases of the game.

Case Studies or Notable Matches

A breakthrough game occurred during the season finale against [Opponent Team], where RoKi executed a stunning comeback victory that secured their playoff spot. This match highlighted their resilience and tactical acumen.

Tips & Recommendations for Analyzing RoKi or Betting Insights 💡

• Analyze head-to-head records to identify patterns that may influence betting odds.
• Closely monitor player injuries or suspensions that could impact team performance.
• Leverage statistics on power-play efficiency when considering bets on special situations.

“RoKi’s strategic depth makes them unpredictable opponents on any given day.” – Expert Analyst Jane Doe

Pros & Cons of RoKi’s Current Form or Performance ✅❌

• ✅ Pro: Strong offensive line capable of turning games around swiftly.
• ❌ Con: Occasional defensive vulnerabilities can be exploited by sharp opponents.

Betting Analysis Guide for RoKi 📊

1. Analyze recent form trends to predict potential outcomes against upcoming opponents.
2. Evaluate key player performances using advanced metrics like Corsi ratings or plus-minus stats.</l[0]: import os
   [1]: import json
   [2]: import numpy as np
   [3]: from sklearn.model_selection import train_test_split

   [4]: class Dataset:
   [5]: def __init__(self):
   [6]: self._X = None
   [7]: self._Y = None
   [8]: self._y_names = None

   [9]: def load(self):
   [10]: raise NotImplementedError()

   [11]: @property
   [12]: def X(self):
   [13]: return self._X

   [14]: @property
   [15]: def Y(self):
   [16]: return self._Y

   [17]: @property
   [18]: def y_names(self):
   [19]: return self._y_names

   import os
   import json
   import numpy as np
   from sklearn.model_selection import train_test_split

   class Dataset:

   """
   Class representing dataset.
   """

   class Dataset:

   # TODO : Implement load() method

   # TODO : Implement X property getter

   # TODO : Implement Y property getter

   # TODO : Implement y_names property getter

   ***** Tag Data *****
   ID: 1
   description: Class definition for Dataset with placeholders for methods load(), X,
   Y, y_names getters which are intended to be implemented later.
   start line: 4
   end line: 19
   dependencies:
   – type: Class
   name: Dataset
   start line: 4
   end line: 19
   context description: This snippet provides an outline of a class designed to represent,
   load, and access data within a dataset. The actual implementation details are missing,
   making it challenging to understand its full functionality without additional context.
   algorithmic depth: 4
   algorithmic depth external: N
   obscurity: 4
   advanced coding concepts: 4
   interesting for students: 5
   self contained: N

   *************
   ## Suggestions for complexity

   1. **Lazy Loading**: Modify the `load` method so that it only loads data when it is accessed via properties (`X`, `Y`, `y_names`). This requires implementing lazy loading techniques.

   2. **Data Validation**: Add validation logic within setters or property getters to ensure that `_X`, `_Y`, `_y_names` adhere to certain constraints (e.g., types, shapes).

   3. **Dynamic Data Augmentation**: Allow dynamic augmentation of data before returning it through properties based on user-defined transformations.

   4. **Parallel Processing**: Implement parallel processing capabilities within the `load` method to handle large datasets efficiently.

   5. **Custom Serialization/Deserialization**: Provide custom serialization/deserialization methods allowing saving/loading datasets into/from various formats (e.g., CSV, JSON).

   ## Conversation

   Hello AI assistant! I am working on this new project where I need a dataset class similar but more advanced than what I used before Can you help me enhance my existing code? Here’s what I have so far:

   python
   class Dataset:
   def __init__(self):
   self._X = None
   self._Y = None
   self._y_names = None

   def load(self):
   raise NotImplementedError()

   @property
   def X(self):
   return self._X

   @property
   def Y(self):
   return self._Y

   @property
   def y_names(self):
   return self._y_names

   Can we add lazy loading here?

   Sure! Lazy loading means we only load data when it’s actually accessed rather than at initialization time. Here’s how you can modify your class:

   python
   class Dataset:
   def __init__(self):
   self._X = None
   self._Y = None
   self._y_names = None

   # Flags indicating if data has been loaded yet.
   self._is_loaded_X = False
   self._is_loaded_Y = False
   self._is_loaded_y_names = False

   def load(self):
   raise NotImplementedError()

   @property
   def X(self):
   if not self._is_loaded_X:
   # Load _X here if not already loaded.
   # Example placeholder logic; replace with actual loading mechanism.
   if not hasattr(self, ‘_data’):
   raise ValueError(“Dataset data is not initialized.”)
   if not hasattr(self, ‘_features’):
   raise ValueError(“Features are not defined.”)
   self._X = np.array([d[self._features] for d in self._data])
   self._is_loaded_X = True

   return self._X

   @property
   def Y(self):
   if not self._is_loaded_Y:
   # Load _Y here if not already loaded.
   if not hasattr(self, ‘_data’):
   raise ValueError(“Dataset data is not initialized.”)
   if not hasattr(self, ‘_target’):
   raise ValueError(“Target feature is not defined.”)
   self._Y = np.array([d[self._target] for d in self._data])
   self._is_loaded_Y = True

   return self._Y

   @property
   def y_names(self):
   if not self._is_loaded_y_names:
   # Load _y_names here if not already loaded.
   # Example placeholder logic; replace with actual loading mechanism.
   if hasattr(self, ‘_target’):
   # Assuming target is always present once set.
   possible_targets_set = set()
   for d in getattr(self, ‘_data’, []):
   possible_targets_set.add(d.get(‘target’, ”))
   if len(possible_targets_set) == 1:
   target_name = possible_targets_set.pop()
   target_name_list =[target_name]
   target_name_list.sort()
   target_name_str= ‘, ‘.join(target_name_list)
   print(f’y names are {target_name_str}’)
   print(f’y names length {len(target_name_list)}’)
   print(f’y names type {type(target_name_str)}’)
   print(‘——–‘)
   print(type(target_name))
   print(‘——–‘)
   print(type(target_name_list))
   print(‘——–‘)

   target_type_dict={‘type’: type(target_name)}

   print(type(target_type_dict))
   print(‘——–‘)
   print(type(target_type_dict[‘type’]))
   print(‘——–‘)

   # Placeholder attributes required by lazy loading logic above.
   # You will need real implementations based on your dataset structure.
   _dataset_instance_data_just_for_example_purposes_=_None_
   _dataset_instance_features_just_for_example_purposes_=_None_
   _dataset_instance_target_just_for_example_purposes_=_None_

   # Example usage after setting up real data structure:
   dataset_instance=Dataset()
   dataset_instance.load() # Should properly initialize _data_, _features_, _target_
   print(dataset_instance.X)
   print(dataset_instance.Y)
   print(dataset_instance.y_names)

   In this version:

   – We added flags `_is_loaded_X`, `_is_loaded_Y`, `_is_loaded_y_names` to keep track of whether each piece of data has been loaded yet.
   – In each property (`X`, `Y`, `y_names`), we check these flags before attempting to load data.

   You’ll need to replace placeholder logic inside these properties with your actual data-loading mechanism.

   Would you like further modifications or explanations about this approach?
   1]
   from typing import AnyStr

   import torch.nn.functional as F

   from .utils import get_device_of_tensor_or_none

   def log_softmax(x,
   dim=None,
   dtype=None,
   layout=None,
   device=None,
   requires_grad=False) -> torch.Tensor:
   “””Logarithm of softmax activation function.”””
   res_dim_type_tuple_size_check(
   res_dim_type_tuple=(dim,),
   expected_size=1)

   res_dtype_layout_device_requires_grad_check(
   res_dtype_layout_device_requires_grad=(dtype,
   layout,
   device,
   requires_grad),
   tensor=x)

   x_max_value_in_dim_unsqueezed_to_dims_after_dim =
   x.max(dim=dim).values.unsqueeze(dim)
   x_max_value_in_dim_unsqueezed_to_dims_after_dim =
   x_max_value_in_dim_unsqueezed_to_dims_after_dim.expand_as(x)

   log_sum_exp_x_minus_x_max_value_in_dim_unsqueezed_to_dims_after_dim =
   torch.log(torch.sum(torch.exp(x –
   x_max_value_in_dim_unsqueezed_to_dims_after_dim),
   dim=dim))

   result =
   x –
   x_max_value_in_dim_unsqueezed_to_dims_after_dim –
   log_sum_exp_x_minus_x_max_value_in_dim_unsqueezed_to_dims_after_dim.unsqueeze(dim)

   result_resized_by_moving_dimension_with_index_of_dimension_over_which_log_softmax_is_computed_from_the_end_of_dimensions_to_the_beginning_of_dimensions =
   result.movedim(result.ndim + dim % result.ndim – dim – (-result.ndim), -result.ndim)

   result_resized_by_moving_dimension_with_index_of_dimension_over_which_log_softmax_is_computed_from_the_end_of_dimensions_to_the_beginning_of_dimensions.unmovedim(-result.ndim,
   result.ndim + dim % result.ndim – dim – (-result.ndim))

   dtype_layout_device_requires_grad_checked_result =
   convert_tensor(resized_result_resized_by_moving_dimension_with_index_of_dimension_over_which_log_softmax_is_computed_from_the_end_of_dimensions_to_the_beginning_of_dimensions,
   dtype=dtype,
   layout=layout,
   device=device,
   requires_grad=requires_grad)

   return dtype_layout_device_requires_grad_checked_result

   def softmax(x,
   dim=None,
   dtype=None,
   layout=None,
   device=None,
   requires_grad=False) -> torch.Tensor:
   “””Softmax activation function.”””
   res_dim_type_tuple_size_check(
   res_dim_type_tuple=(dim,),
   expected_size=1)

   res_dtype_layout_device_requires_grad_check(
   res_dtype_layout_device_requires_grad=(dtype,
   layout,
   device,
   requires_grad),
   tensor=x)

   log_sum_exp_x_minus_x_max_value_in_dim_unsqueezed_to_dims_after_dim =
   torch.log(torch.sum(torch.exp(x –
   x_max_value_in_dim_unsqueezed_to_dims_after_dim),
   dim=dim))

   exp_x_minus_x_max_value_in_dim_unsqueezed_to_dims_after_and_log_sum_exp_subtracted =
   torch.exp(x –
   x_max_value_in_dim_unsqueezed_to_dims_after_dim –
   log_sum_exp_x_minus_x_max_value_in_dim_unsqueezed_to_dims_after_dim.unsqueeze(dim))

   result =
   exp_x_minus_x_max_value_in_dim_unsqueezed_to_dims_after_and_log_sum_exp_subtracted.unmovedim(-exp_x_minus_x_max_value_in_dim_unsqueezed_to_dims_after_and_log_sum_exp_subtracted.ndim,

   result_resized_by_moving_dimension_with_index_of_dimension_over_which_log_softmax_is_computed_from_the_end_of_dimensions_to_the_beginning_of_dimensions.ndim +
   dim % exp_x_minus_x_max_value_in_dime_ndimsu_ed_todimsafter_and_logsumexpsubractedndimsu_ed_todimsafter.dim –

   dim –

   (-exp_x_minus_x_max_valeu_indim_ndimsu_ed_todimsafter_and_logsumexpsubractedndimsu_ed_todimsafter.ndi))

   dtype_layout_device_requires_grad_checked_result =
   convert_tensor(result_resized_by_moving_dimension_with_index_of_dimension_over_which_softmax_is_computed_from_the_end_of_dimensions_to_the_beginning_of_dimensions,

   dtype=dtype,

   layout=layout,

   device=device,

   requires_grad=requires_grad)

   return dtype_layout_device_requires_grad_checked_result

   def one_hot(indices_,
   num_classes_,
   *,
   bool_output=True) -> torch.Tensor:

   indices_num_classes_one_hot_shape_tuple_size_check(
   indices_num_classes_one_hot_shape_tuple=(indices_,
   num_classes_,
   bool_output),
   expected_size=3)

   indices_num_classes_one_hot_values_check((indices_,
   num_classes_,
   bool_output),
   allowed_values=((True,),
   (False,),
   ((0,),
   (0,),
   (False))),
   )

   indices_num_classes_bool_output_int_dtype_checked_indices_int_dtype_checked_num_classes_bool_output_int_dtype_checked_bool_output_int_dtype_checked_bool_output =

   int(dtype(indices.dtype)) * int(bool(bool_output))

   one_hot_tensor_data_filling_function_for_indices_num_classes_bool_output_int_dtype_checked_indices_int_dtype_checked_num_classes_bool_output_int_dtype_checked_bool_output_int_dtype_checked_bool_output =

   lambda i_j_k_l_indices_num_classsees_s_i_j_k_l_indices_numclassesboolouttputintdtypescheckedindicesintdtypecheckednumclassesbooloutputintdtypecheckedbooloutputintdtypecheckedbooloutput:

   int(i_j_k_l_indices_numclassesboolouttputintdtypescheckedindicesintdtypecheckednumclassesbooloutput == j) * k * l * indices_numclassesboolouttputintdtypescheckedindicesintdtypecheckednumclassesbooloutput.int()

   one_hot_tensor_data_filling_function_for_indices_num_classes_bool_output_int_dtype_checked_indices_int_dtype_checked_num_classes_bool_output_int_dtype_checked_bool_output_int_dtype_checked_bool_outtput.callable_or_none_check(one_hot_tensor_data_filling_function_for_indices_num_classes_bool_outptut_int_dtpe_checkd_indces_nt_dtpe_checjnd_classse_boloutptutnt_dtpe_chcekd_boool_outptut)

   one_hot_tensor_data_filling_function_for_indices_num_classes_bool_outptut_int_dtpe_chckd_indces_nt_dtpe_checjnd_classse_boloutptutnt_dtpe_chcekd_boool_outptut.callable_arguments_count_check(one_hot_tensor_data_filling_function_for_indices_num_classsees_bolooutpuittypetypedtecheckindcesntypedtechecjnd_classessbolooutpuittypetypedtechecjbooolouputtypetypedtechecjbooolouput,

   expected_argument_count=4)

   one_hot_tensor_shape_creation_function_for_indices_number_cassses_boolean_oatput_integer_datatype_chechcied_indsices_integer_datatype_cheched_numbers_cassses_boolean_oatput_integer_datatype_cheched_boolean_oatput_integer_datatype_cheched_boolean_oatput =

   lambda indices_number_cassses_boolean_oatput_integer_datatype_chechcied_indsices_integer_datatype_cheched_numbers_cassses_boolean_oatput_integer_datatype_cheched_boolean_oatput_integer_datatype_cheched_boolean_oatput:

   tuple(indices_number_casssesbooleanoatputintegetdatatypechechciedindcesintegetdatayypechechednumberscassesbooleanoatputintegerdatatypechechedboooloautpritypedateypechecktedboooloautpritypedateypecheckted.shape[:-onehottensorshapecreationfunctionforindexenumberclassesebooleanoautpotupinterdatatyptypecheccidindecesinterdatayptecheccenumberclassesebooleanoautpotupinterdatayptecheceboooloautpotupinterdatayptecheceboooloautpotup]+(numberscassesbooleanoautpotupinterdatayptecheced,) + (onehottensorshapecreationfunctionforindexenumberclassesebooleanoautpotupinterdatatyptecheccidindecesinterdatayptecheccenumberclassesebooleanoautpotupinterdatayptecheceboooloautpotupinterdatayptecheceboooloautpotup,) )

   one_hot_tensor_shape_creation_function_for_indiced_numbre_classees_boolea_out_put_inter_dtape_checed_indexes_inter_dtae_checed_numbre_classeess_boolea_out_put_inter_dtae_checed_boolea_out_put_inter_dtae_checed_boolea_out_put.callable_or_none_check(one_hot_tensor_shape_creation_function_for_indiced_numbre_classees_boolea_out_put_inter_dtae_checed_indexes_inter_dtae_checed_numbre_classeess_boolea_out_put_inter_dtae_checed_boolea_out_put_inter_dtae_checed_boolea_out_put)

   one_hot_tensor_shape_creation_function_for_indiced_numbre_classees_boolea_out_put_inter_dtae_checed_indexes_inter_dtae_checed_numbre_classeess_boolea_out_put_inter_dtae_checed_boolea_out_put_inter_dtae_checed_boolea_out_put.callable_arguments_count_check(one_hot_tensor_shape_creation_function_for_indiced_numbre_classees_boloouot_inpupterdateypechecktedindicesteerdateypechecktednumberclasseesebeleouot_inpupterdateypechecktednumberclasseesebeleouot_inpupterdateypechecktedbeleouot_inpupterdateypecheckted,

   expected_argument_count=4)

   one_hot_created_using_view_and_fill_method_using_onehottensorshapcreationfunctionforindexenumberclassesebooleanoauttonterdatatyptechedindextenterdatatyptechednumberclasseebolaoutpoiterdaeteypetechedbolaaoutpoiterdaetypechectebolaoutpoiterdaetypechectebolaoutpoiterdaetypechetct =

   torch.empty(size=

   onehottensorshapcreationfunctionforindexenumberclasseeboauttonterdaeteypetechedindextenterdaeteypetechednumberclasseeboauttonterdaeteypetechedboauttonterdaeteypetechedboauttonterdaeteypetchect(

   indices_number_cassses_boloautpoitnt_eatedindecseintegetdataptechetnuednumbercasseebolaootpuinterdataptechetnuedbolaootpuinterdataptechetnuedbolaootpuinterdataptechetnued),

   dtype=

   bool(bolaoautoitnt_eatedindecseintegetdataptechetnued.bool()) +

   int(bolaoautoitnt_eatedindecseintegetdataptechetnued.int()) +

   num_classsse_bealooauoptiputerdataptyepetechec().long(),

   device=

   get_devicfo_teore_or_none(indice_seentege_typetechect())

   ).fill_(onehottenrdatafillingfucntionforindexenumbercasseebolaoautoputerdatatypechtctindicesteerdatatypechtctnumbercasseebolaoautoputerdatatypechtctbolaoautoputerdatatypechtctbolaoautoputerdatatypechtct)

   view_as_onehot_viewing_shaped_like_original_input_if_possible_else_viewing_shaped_like_original_input_but_stripping_off_last_two_elements =

   lambda t:

   t.view(*indices_number_cassses_boloautoiptuteatedindecseintegetdatttypetechect.shape[:-onehotviewashapeledlikeoriginlinputifposssibleelseviewashapeledlikeoriginlinputbutstrippingofflasttwoelements()])

   view_as_onehot_viewing_shaped_like_original_input_if_possible_else_viewing_shaped_like_original_input_but_stripping_off_last_two_elements_callable_or_none_check(view_as_onehot_viewing_shaped_like_original_input_if_possible_else_viewing_shaped_like_original_input_but_stripping_off_last_two_elements)

   view_as_onehot_viewing_shaped_like_original_input_if_possible_else_viewing_shaped_like_original_input_but_stripping_off_last_two_elements_callable_arguments_count_check(view_as_onehot_viewing_shaped_like_original_input_if_possible_else_viewing_shaped_like_original_input_but_striping_off_last_two_element,

   expected_argument_count=1)

   boolean_return_valus_when_converting_a_non_zero_scalar_into_a_numpy_array_returns_a_non_zero_scalar_when_converting_a_non_zero_scalar_into_an_numpy_array_returns_a_non_zero_scalar_when_converting_a_non_zero_scalar_into_an_numpy_array_returns_a_non_zero_scalar_when_converting_a_non_zero_scalar_into_an_numpy_array_returns_a_non_zero_scalar =

   lambda scalar:

   bool(numpy.array(scalar).item())

   boolean_return_valus_when_converting_a_non_zero_scalar_into_an_numpy_array_returns_a_non-zero-scalar_callable_or_none_check(boolean_return_valus_when_converting_a_non-zero-scalar_into_an_numpy_array_returns_a-non-zero-scalar)

   boolean_return_valus_when_converting_a_non-zero-scalar_into_an_numpy_array_returns-a-non-zero-scalar_callable_arguments_count_check(boolean_return_valus_when_converting_a-non-zero-scalar_into_an_numpy_array_returns-a-non-zero-scalar,

   expected_argument_count=1)

   converted_result_based_on_whether_or_not_conversion_was_successful_based_on_whether_or_not_conversion_was_successful_based_on_whether_or_not_conversion_was_successful_based_on_whether_or_not_conversion_was_successful_based_on_whether_or_not_conversion_was_successful =

   lambda original_object,

   conversion_attempt_fn:

   conversion_attempt_fn(original_object) *

   original_object.nelement() *

   original_object.element_size()

   converted_result_based_on_whether_or_not_conversion_was_successful_based_on_whether_or_not_conversion_was_successful_based_on_whether_or_not_conversion_was_successful_based_on_whether_or_not_conversion_was_successful_based_on_whether_or_not_conversion_wast_successfu_callable_arguments_count_check(converted_result_based_on_wheterornotconversionwassuccessfulbasedonwheterornotconversionwassuccessfulbasedonwheterornotconversionwassuccessfulbasedonwheterornotconversionwassuccessfulbasedonwheterornotconversionwassuccessful,

   expected_argument_count=2)

   converted_result_based_on_wheterornotconversionwasuccessfulbasedonwheterornotconversionwasuccessfulbasedonwheterornotconversionwasuccessfulbasedonwheterornotconversionwasuccessfulbasedonwheterornotconversonsuccessfull.data_ptr() == view_as_onehot_viewing_shaped_like_original_input_if_possible_else_viewing_shaped_like_original_input_but_stripping_off_last_two_element(converted_result_based_on_whaterornotconversonsuccessfull).data_ptr()

   converted_result_based_on_wheterornotconversonsuccessfull.view_as(original_object).

   def gelu(input_: torch.Tensor):

   geludatafillfn_lambda_funciton_geluinputtensortensorinputtensortensorinputtensorlambda_funciton_geluinputtensortensorinputtensortensorinputtensorlambda_funciton_geluinputtensortensorinputtensortensorinputtensorlambda_funciton_geluinputtensorescaled_erf_plus_half_times_input =

   lambda i:

   ((torch.erf(i / math.sqrt(2))) + (

   float(i.nelement()) / float(i.element_size())) / (

   math.sqrt(math.pi)))

   geludatafillfn_lambda_funciton_geluinputtensortensorinputtensortensorinputtensorlambda_funciton_geluinputtensortensorinputtensortensorinputtensorlambda_funciton_geluinputtensorescaled_erf_plus_half_times_input.callable_argscountheck(geludatafillfn_lambda_funciton_geluinputtensorescaled_erf_plus_half_times_inputs_lambda_funciton_geluinputscaled_erf_plus_half_times_inputs_lambda_funciton_geluuinputscaled_erf_plus_half_times_inputs_lambda_funciton_geluuinputscaled_erf_plus_half_times_inputs_lambda_funciton_geluuinputscaled_erf_plus_half_times_inputs_lambda_funciton_geluuinputscaled_erf_plus_half_times_inputs,

   expected_argcount_expected_argcount_expected_argcount_expected_argcount_expected_argcount_expected_argcount_expected_argcount_expected_argcount_expected_argcount_expected_argcount_expected_argcount_expectecdargcountsexpectedargcountsexpectedargcountsexpectedargcountsexpectedargcountsexpectedargcountsexpectedargcountsexpectedargcountsexpectedargcountsexpectedarccountexpectedargcounts==>

   )

   )

   geludatafillfn_lambda_funciton_geluinputscaled_erfplushalftimesinputs_lambdafuncitiongelnputscalederfhalftimesinputs_lambdafuncitiongelnputscalesderfhalftimesinputs_lambdafuncitiongelnputscalesderfhalftimesinputscallablereqargs_len(geludatafillfn_lambdafuncitiongelnputscalesderfhalftimesinputs_lambdafuncitiongelnputscalesderfhalftimesinputs_lambdafuncitiongelnputscalesderfhalftimesinputs_lambdafuncitiongelunputscalesderfhalftimesinputs_lambdafuncitiongelunputscalesderfhalftimesinputs_lambdafuncitiongelunputscalesderfhalftimes inputs_lambdafuncitiongelunputscalesderfhalftimes inputs)==>

   )

   geludatafillfn_scaled_erfplushalftimestensorsizecreatetheshapetensorsizeshapecreatingfunctio_scaled_erfscalehalftimestensorsizeshapingfunscaled_erfscalehalftimestensorsizeshapingfunscaled_erfscalehalftimestensorsizeshapingfunscaled_erfscalehalftimestensorsizeshapingfunscaled_erfscalehalftimestensorsizeshapingfunscaled_erfscalehalftimestensorsizecreatheshapetensorshapecreatingfunctions=lambda input:

   tuple(input.size()[:-geludatafillfn_scalederfscalehalftimesizecreatheshapetensorsizeshapingfunctions()] + (

   input.size()[-geludatafilltn_scalederfscalehalftimesizecreatheshapetensorsizeshapingfunctions():]))

   geludatafillfn_scalederfscalehalftimesizecreatheshapetensorsizeshapingfunscaldderedfscalehalftimesizecreatheshapetensorsizeshapingfunscaldderedfscalehalftimesizecreatheshapetensorsizeshapingfunscaldderedfscalehalftimesizecreatheshapetensorsizescalingfunscaldderedfscalehalftimesizecreathershapesizingfunctionscaldderedfscalehalftimesizeshapecreatingfunctionscallable(geludatafillfn_scalederdffscalehalftimestencsizecreathtershapetsizenosingfunctions)

   geludatafillfn_scalederrffscalehalftimecreatehsapesizingfunctions_callabale_args_count(geludatafilfn_scalederrffscalehalftimcreatehsapesizingfunctions,

   expeceted_arig_counts_expeceted_arig_counts_expeceted_arig_counts_expeceted_arig_counts_expeceted_arig_counts_expeceted_arig_counts_expeceted_arig_counts_expeceted_arig_counts_expeceted_arig_counts_expectecdargsarigsexpectecdargsarigs==>

   )

   )

   scaled_errffscalehtimecreatehsapecreatedwith_emptyfilledwith_scaldederrffscalehtimecreatehsapecreatedwith_emptyfilledwith_scaldederrffscalehtimecreatehsapecreatedwith_emptyfilledwith_scaldederrffscalehtimecreatehsapecreatedwith_emptyfilledwith_scaldederrffscalehtimecreatehsapecreatedwith_emptyfilledwith_scaldederrffscalehtimecreatehsapecreatedwith_emptyfilledwith_scaldederrffscalehtimecreatehsapecreated_withempty_filledwithe_scaledderrffscalehtime_create_hsapeshapecreating_functions_filled_with_scaledderrff_scale_htime_create_hsa_pe_scaling_functions_filled_with_scaleddrrfffsscalhe_time_create_hspae_scalling_functions_filled_wih_scaledeeeefsrfeehalftime_create_hspaee_scalling_functions_filled_wih_scaledeeeefsrfeehalftime_create_hspaee_scalling_functions_filled_wih_scaledeeeefsrfeehalftime_create_hspaee_scalling_functions_filled_wih_scaledeeeefsrfeehalftime_create_hspaee_scalling_functions_filled_wih_scaledeeeeefsrfhhalthme_create_hsapaeescaling_functions_fillwedwithe_scaledeeeeeefsrfehhalthme_cre_hsapaeescaling_functions_fillwedwithe_scalddeeefsrfehhalthme_cre_hsapaeescaling_functions_fillwedwithe_scalddeeefsrfehhalthme_cre_hsapaeescaling_functions_fillwedwithe_scalddeeefsrfehhalthme_cre_hsapaeescaling_functions_fillwedwithe_scalddeeefsrfhhaltme_cre_hsapaeescalldng_functons_fillweide_scalddeeefsrfhhaltme_cre_hsapaeescalldng_functons_fillweide_scalddeeefsrfhhaltme_cre_hsapaeescalldng_functons_fillweide_scalddeeefsrfhhaltme_cre_hsapaeescalldng_functons_fillweide_scalddeeefsrfhhaltme_cre_hsapaeescalldng_functons_fillweide_scalddeeefsrfhhaltme_cre_hsapaee:

   empty(size=scaled_errfrsfhaelftimcszescrtahshpszecreatnsghpszescrtahshpszecreatnsghpszescrtahshpszecreatnsghpszescrtahshpszecreatnsghpszescrtahshpszecreatnsghpszescrtahshpszecreatnsghpszescrtahshpszecreatnsghpszs(),

   dtyp=bolt(bool(input.bool())) + int(int(input.int())) ,

   dev=getdevicofteornone(input)).

   viewasoriginalifpossibleelseviewasoriginalbutstrippingofflastelement=lambda t:

   t.view(*tuple(t.size()[:-viewasoriginalifpossibleelseviewasoriginalbutstrippofflastelement()]))

   viewasoriginalifpossibleelseviewasoriginalbutstrippofflastelementcallabale(argumcntchk(viewasoriginalifpossibleelseviewasoriginalbutstripofflastelement))

   viewasoriginalifpossibleelseviewasoriginalbutstripofflastelementcallabale(argumcntchk(viewasoriginalifpossibleelseviewastripofflastelement)==>

   argumcntchk(viewastripofflastelement)==>

   )

   valuenewzeroscalarintoanarrayreturnsnonzeroscalarnewzeroscalerintoanarrayreturnsnonzeroscalerintoanarrayreturnsnonzeroscalerintoanarrayreturnnonzeroscalerintoanarrayreturnsnonzerovaluenewzerocalscrainararyreturnscalerintoanarrayreturnsnonzerovaluenewzerovaluenewzerovaluenewzerovaluenewzerovaluenewzerovalue_newzeroscalereturnsanarrayreturnscalerintoanarryretunsnonzerovalue_newzeroscalereturnsanarrayreturnscalerintoanarryretunsnonzerovalueneewzeroscalereturnsanarrayreturnscalerintoanarryretunsnonzerovalueneewzeroscalereturnsanarrayreturnscalerinto