### 2. Code

Provide code to help us reproduce your issues using one of the following options:

#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

import org.jetbrains.kotlin.analysis.api.types.KtCapturedType
import org.jetbrains.kotlin.analysis.api.types.KtTypeNullability
import org.jetbrains.kotlin.resolve.calls.inference.CapturedType

internal class KtFe10CapturedType(
    override val fe10Type: CapturedType,
    override val analysisContext: Fe10AnalysisContext
) : KtCapturedType(), KtFe10Type {

import org.jetbrains.kotlin.analysis.api.symbols.pointers.KtSymbolPointer
import org.jetbrains.kotlin.analysis.api.types.KtType
import org.jetbrains.kotlin.descriptors.FunctionDescriptor
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing
import org.jetbrains.kotlin.resolve.descriptorUtil.isExtension

internal class KtFe10DescAnonymousFunctionSymbol(
    override val descriptor: FunctionDescriptor,

import org.jetbrains.kotlin.descriptors.PropertyGetterDescriptor
import org.jetbrains.kotlin.descriptors.hasBody
import org.jetbrains.kotlin.name.CallableId
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing

internal class KtFe10DescPropertyGetterSymbol(
    override val descriptor: PropertyGetterDescriptor,
    override val analysisContext: Fe10AnalysisContext

import org.jetbrains.kotlin.analysis.api.types.KtType
import org.jetbrains.kotlin.name.CallableId
import org.jetbrains.kotlin.name.Name
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing
import org.jetbrains.kotlin.resolve.descriptorUtil.isExtension
import org.jetbrains.kotlin.resolve.sam.SamConstructorDescriptor
import org.jetbrains.kotlin.resolve.sam.SamTypeAliasConstructorDescriptor

        constructor: (KtParameterSymbol) -> T
    ): T = constructor(variableReference.descriptor.toKtSymbol(analysisContext) as KtParameterSymbol)

    // Util function to avoid hard coding names of the classes. Type inference will do a better job figuring out the best type to cast to.
    // This visitor isn't type-safe anyway

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack

            }
        else firFunctionSymbol.valueParameters[valueParameterReference.parameterIndex]
    )

    // Util function to avoid hard coding names of the classes. Type inference will do a better job figuring out the best type to cast to.
    // This visitor isn't type-safe anyway
    private inline fun <reified T> ConeContractDescriptionElement.accept() =

“parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and...

import org.jetbrains.kotlin.psi.*
import org.jetbrains.kotlin.psi.psiUtil.*
import org.jetbrains.kotlin.resolve.BindingContext
import org.jetbrains.kotlin.resolve.calls.components.isVararg
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing
import org.jetbrains.kotlin.resolve.calls.smartcasts.MultipleSmartCasts
import org.jetbrains.kotlin.resolve.calls.smartcasts.SingleSmartCast
import org.jetbrains.kotlin.resolve.calls.util.getParameterForArgument