def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Optional[int]:



      '''simple docstring'''

      _lowerCamelCase           =					(boundary[1] - boundary[0]) / steps
      _lowerCamelCase           =					boundary[0]
      _lowerCamelCase           =					boundary[1]
      _lowerCamelCase           =					make_points(lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_		)
      _lowerCamelCase           =					0.0
      y += (h / 3.0) * f(lowerCamelCase_		)
      _lowerCamelCase           =					2
      for i in x_i:
            y += (h / 3) * (4 - 2 * (cnt % 2)) * f(lowerCamelCase_		)
            cnt += 1
      y += (h / 3.0) * f(lowerCamelCase_		)
      return y
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     Any:



      '''simple docstring'''

      _lowerCamelCase           =					a + h
      while x < (b - h):
            yield x
            _lowerCamelCase           =					x + h
def    __magic_name__(	__UpperCAmelCase		)    ->     Tuple:  # enter your function here



      '''simple docstring'''

      _lowerCamelCase           =					(x - 0) * (x - 0)
      return y
def    __magic_name__(	)    ->     List[Any]:



      '''simple docstring'''

      _lowerCamelCase           =					0.0  # Lower bound of integration
      _lowerCamelCase           =					1.0  # Upper bound of integration
      _lowerCamelCase           =					1_0.0  # define number of steps or resolution
      _lowerCamelCase           =					[a, b]  # define boundary of integration
      _lowerCamelCase           =					method_a(lowerCamelCase_			,    lowerCamelCase_		)
      print(F'y = {y}'		)


if __name__ == "__main__":
     main()

"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase		:    Tuple			=       {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    int			=       ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

			lowercase		:    Optional[Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
