public org.tensorflow.framework.MemoryLogTensorOutput buildPartial() { org.tensorflow.framework.MemoryLogTensorOutput result = new org.tensorflow.framework.MemoryLogTensorOutput(this); result.stepId_ = stepId_; result.kernelName_ = kernelName_; result.index_ = index_; if (tensorBuilder_ == null) { result.tensor_ = tensor_; } else { result.tensor_ = tensorBuilder_.build(); } onBuilt(); return result; }
public Builder mergeFrom(org.tensorflow.framework.MemoryLogTensorOutput other) { if (other == org.tensorflow.framework.MemoryLogTensorOutput.getDefaultInstance()) return this; if (other.getStepId() != 0L) { setStepId(other.getStepId()); } if (!other.getKernelName().isEmpty()) { kernelName_ = other.kernelName_; onChanged(); } if (other.getIndex() != 0) { setIndex(other.getIndex()); } if (other.hasTensor()) { mergeTensor(other.getTensor()); } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; }
public org.tensorflow.framework.MemoryLogTensorOutput build() { org.tensorflow.framework.MemoryLogTensorOutput result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; }
/** * <pre> * Name of the kernel producing an output as set in GraphDef, e.g., * "affine2/weights/Assign". * </pre> * * <code>string kernel_name = 2;</code> */ public Builder clearKernelName() { kernelName_ = getDefaultInstance().getKernelName(); onChanged(); return this; } /**
@java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } if (!(obj instanceof org.tensorflow.framework.MemoryLogTensorOutput)) { return super.equals(obj); } org.tensorflow.framework.MemoryLogTensorOutput other = (org.tensorflow.framework.MemoryLogTensorOutput) obj; boolean result = true; result = result && (getStepId() == other.getStepId()); result = result && getKernelName() .equals(other.getKernelName()); result = result && (getIndex() == other.getIndex()); result = result && (hasTensor() == other.hasTensor()); if (hasTensor()) { result = result && getTensor() .equals(other.getTensor()); } result = result && unknownFields.equals(other.unknownFields); return result; }
public void writeTo(com.github.os72.protobuf351.CodedOutputStream output) throws java.io.IOException { if (stepId_ != 0L) { output.writeInt64(1, stepId_); } if (!getKernelNameBytes().isEmpty()) { com.github.os72.protobuf351.GeneratedMessageV3.writeString(output, 2, kernelName_); } if (index_ != 0) { output.writeInt32(3, index_); } if (tensor_ != null) { output.writeMessage(4, getTensor()); } unknownFields.writeTo(output); }
/** * <pre> * Output tensor details. * </pre> * * <code>.tensorflow.TensorDescription tensor = 4;</code> */ public org.tensorflow.framework.TensorDescriptionOrBuilder getTensorOrBuilder() { return getTensor(); }
public org.tensorflow.framework.MemoryLogTensorOutput getDefaultInstanceForType() { return org.tensorflow.framework.MemoryLogTensorOutput.getDefaultInstance(); }
public int getSerializedSize() { int size = memoizedSize; if (size != -1) return size; size = 0; if (stepId_ != 0L) { size += com.github.os72.protobuf351.CodedOutputStream .computeInt64Size(1, stepId_); } if (!getKernelNameBytes().isEmpty()) { size += com.github.os72.protobuf351.GeneratedMessageV3.computeStringSize(2, kernelName_); } if (index_ != 0) { size += com.github.os72.protobuf351.CodedOutputStream .computeInt32Size(3, index_); } if (tensor_ != null) { size += com.github.os72.protobuf351.CodedOutputStream .computeMessageSize(4, getTensor()); } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; }
/** * <pre> * Name of the kernel producing an output as set in GraphDef, e.g., * "affine2/weights/Assign". * </pre> * * <code>string kernel_name = 2;</code> */ public Builder clearKernelName() { kernelName_ = getDefaultInstance().getKernelName(); onChanged(); return this; } /**
/** * <pre> * Output tensor details. * </pre> * * <code>.tensorflow.TensorDescription tensor = 4;</code> */ public org.tensorflow.framework.TensorDescriptionOrBuilder getTensorOrBuilder() { return getTensor(); }
public org.tensorflow.framework.MemoryLogTensorOutput getDefaultInstanceForType() { return org.tensorflow.framework.MemoryLogTensorOutput.getDefaultInstance(); }
@java.lang.Override public int hashCode() { if (memoizedHashCode != 0) { return memoizedHashCode; } int hash = 41; hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + STEP_ID_FIELD_NUMBER; hash = (53 * hash) + com.github.os72.protobuf351.Internal.hashLong( getStepId()); hash = (37 * hash) + KERNEL_NAME_FIELD_NUMBER; hash = (53 * hash) + getKernelName().hashCode(); hash = (37 * hash) + INDEX_FIELD_NUMBER; hash = (53 * hash) + getIndex(); if (hasTensor()) { hash = (37 * hash) + TENSOR_FIELD_NUMBER; hash = (53 * hash) + getTensor().hashCode(); } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; }
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException { if (stepId_ != 0L) { output.writeInt64(1, stepId_); } if (!getKernelNameBytes().isEmpty()) { com.google.protobuf.GeneratedMessageV3.writeString(output, 2, kernelName_); } if (index_ != 0) { output.writeInt32(3, index_); } if (tensor_ != null) { output.writeMessage(4, getTensor()); } unknownFields.writeTo(output); }
public org.tensorflow.framework.MemoryLogTensorOutput build() { org.tensorflow.framework.MemoryLogTensorOutput result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; }
public org.tensorflow.framework.MemoryLogTensorOutput buildPartial() { org.tensorflow.framework.MemoryLogTensorOutput result = new org.tensorflow.framework.MemoryLogTensorOutput(this); result.stepId_ = stepId_; result.kernelName_ = kernelName_; result.index_ = index_; if (tensorBuilder_ == null) { result.tensor_ = tensor_; } else { result.tensor_ = tensorBuilder_.build(); } onBuilt(); return result; }
public int getSerializedSize() { int size = memoizedSize; if (size != -1) return size; size = 0; if (stepId_ != 0L) { size += com.google.protobuf.CodedOutputStream .computeInt64Size(1, stepId_); } if (!getKernelNameBytes().isEmpty()) { size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, kernelName_); } if (index_ != 0) { size += com.google.protobuf.CodedOutputStream .computeInt32Size(3, index_); } if (tensor_ != null) { size += com.google.protobuf.CodedOutputStream .computeMessageSize(4, getTensor()); } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; }
@java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } if (!(obj instanceof org.tensorflow.framework.MemoryLogTensorOutput)) { return super.equals(obj); } org.tensorflow.framework.MemoryLogTensorOutput other = (org.tensorflow.framework.MemoryLogTensorOutput) obj; boolean result = true; result = result && (getStepId() == other.getStepId()); result = result && getKernelName() .equals(other.getKernelName()); result = result && (getIndex() == other.getIndex()); result = result && (hasTensor() == other.hasTensor()); if (hasTensor()) { result = result && getTensor() .equals(other.getTensor()); } result = result && unknownFields.equals(other.unknownFields); return result; }
@java.lang.Override public int hashCode() { if (memoizedHashCode != 0) { return memoizedHashCode; } int hash = 41; hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + STEP_ID_FIELD_NUMBER; hash = (53 * hash) + com.google.protobuf.Internal.hashLong( getStepId()); hash = (37 * hash) + KERNEL_NAME_FIELD_NUMBER; hash = (53 * hash) + getKernelName().hashCode(); hash = (37 * hash) + INDEX_FIELD_NUMBER; hash = (53 * hash) + getIndex(); if (hasTensor()) { hash = (37 * hash) + TENSOR_FIELD_NUMBER; hash = (53 * hash) + getTensor().hashCode(); } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; }
public Builder mergeFrom(org.tensorflow.framework.MemoryLogTensorOutput other) { if (other == org.tensorflow.framework.MemoryLogTensorOutput.getDefaultInstance()) return this; if (other.getStepId() != 0L) { setStepId(other.getStepId()); } if (!other.getKernelName().isEmpty()) { kernelName_ = other.kernelName_; onChanged(); } if (other.getIndex() != 0) { setIndex(other.getIndex()); } if (other.hasTensor()) { mergeTensor(other.getTensor()); } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; }